Xianpei Han


2023

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WebDP: Understanding Discourse Structures in Semi-Structured Web Documents
Peilin Liu | Hongyu Lin | Meng Liao | Hao Xiang | Xianpei Han | Le Sun
Findings of the Association for Computational Linguistics: ACL 2023

Web documents have become rich data resources in current era, and understanding their discourse structure will potentially benefit various downstream document processing applications. Unfortunately, current discourse analysis and document intelligence research mostly focus on either discourse structure of plain text or superficial visual structures in document, which cannot accurately describe discourse structure of highly free-styled and semi-structured web documents. To promote discourse studies on web documents, in this paper we introduced a benchmark – WebDP, orienting a new task named Web Document Discourse Parsing. Specifically, a web document discourse structure representation schema is proposed by extending classical discourse theories and adding special features to well represent discourse characteristics of web documents. Then, a manually annotated web document dataset – WEBDOCS is developed to facilitate the study of this parsing task. We compared current neural models on WEBDOCS and experimental results show that WebDP is feasible but also challenging for current models.

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Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension
Ning Bian | Hongyu Lin | Xianpei Han | Ben He | Le Sun
Findings of the Association for Computational Linguistics: EMNLP 2023

Distant Supervision (DS) is a promising learning approach for MRC by leveraging easily-obtained question-answer pairs. Unfortunately, the heuristically annotated dataset will inevitably lead to mislabeled instances, resulting in answer bias and context noise problems. To learn debiased and denoised MRC models, this paper proposes the Contrastive Distant Supervision algorithm – CDS, which can learn to distinguish confusing and noisy instances via confidence-aware contrastive learning. Specifically, to eliminate answer bias, CDS samples counterfactual negative instances, which ensures that MRC models must take both answer information and question-context interaction into consideration. To denoise distantly annotated contexts, CDS samples confusing negative instances to increase the margin between correct and mislabeled instances. We further propose a confidence-aware contrastive loss to model and leverage the uncertainty of all DS instances during learning. Experimental results show that CDS is effective and can even outperform supervised MRC models without manual annotations.

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Does the Correctness of Factual Knowledge Matter for Factual Knowledge-Enhanced Pre-trained Language Models?
Boxi Cao | Qiaoyu Tang | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

In recent years, the injection of factual knowledge has been observed to have a significant positive correlation to the downstream task performance of pre-trained language models. However, existing work neither demonstrates that pre-trained models successfully learn the injected factual knowledge nor proves that there is a causal relation between injected factual knowledge and downstream performance improvements. In this paper, we introduce a counterfactual-based analysis framework to explore the causal effects of factual knowledge injection on the performance of language models within pretrain-finetune paradigm. Instead of directly probing the language model or exhaustively enumerating potential confounding factors, we analyze this issue by perturbing the factual knowledge sources at different scales and comparing the performance of pre-trained language models before and after the perturbation. Surprisingly, throughout our experiments, we find that although the knowledge seems to be successfully injected, the correctness of injected knowledge only has a very limited effect on the models’ downstream performance. This finding strongly challenges previous assumptions that the injected factual knowledge is the key for language models to achieve performance improvements on downstream tasks in pretrain-finetune paradigm.

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Toward Unified Controllable Text Generation via Regular Expression Instruction
Xin Zheng | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning In-context Learning for Named Entity Recognition
Jiawei Chen | Yaojie Lu | Hongyu Lin | Jie Lou | Wei Jia | Dai Dai | Hua Wu | Boxi Cao | Xianpei Han | Le Sun
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To address the above problems, this paper proposes an in-context learning-based NER approach, which can effectively inject in-context NER ability into PLMs and recognize entities of novel types on-the-fly using only a few demonstrative instances. Specifically, we model PLMs as a meta-function Lambda_instruction, demonstrations, text.M, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i.e., (Lambda . M) (instruction, demonstrations) ->F where F will be a new entity extractor F: text -> entities. To inject the above in-context NER ability into PLMs, we propose a meta-function pre-training algorithm, which pre-trains PLMs by comparing the (instruction, demonstration)-initialized extractor with a surrogate golden extractor. Experimental results on 4 few-shot NER datasets show that our method can effectively inject in-context NER ability into PLMs and significantly outperforms the PLMs+fine-tuning counterparts.

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Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing
Shan Wu | Chunlei Xin | Hongyu Lin | Xianpei Han | Cao Liu | Jiansong Chen | Fan Yang | Guanglu Wan | Le Sun
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Current neural semantic parsers take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. Thus, minimizing the supervision effort is one of the key challenges in semantic parsing. In this paper, we propose the Retrieval as Ambiguous Supervision framework, in which we construct a retrieval system based on pretrained language models to collect high-coverage candidates. Assuming candidates always contain the correct ones, we convert zero-shot task into ambiguously supervised task. To improve the precision and coverage of such ambiguous supervision, we propose a confidence-driven self-training algorithm, in which a semantic parser is learned and exploited to disambiguate the candidates iteratively. Experimental results show that our approach significantly outperforms the state-of-the-art zero-shot semantic parsing methods.

2022

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ECO v1: Towards Event-Centric Opinion Mining
Ruoxi Xu | Hongyu Lin | Meng Liao | Xianpei Han | Jin Xu | Wei Tan | Yingfei Sun | Le Sun
Findings of the Association for Computational Linguistics: ACL 2022

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.

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Few-shot Named Entity Recognition with Self-describing Networks
Jiawei Chen | Qing Liu | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Few-shot NER needs to effectively capture information from limited instances and transfer useful knowledge from external resources. In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set. Specifically, we design Self-describing Networks (SDNet), a Seq2Seq generation model which can universally describe mentions using concepts, automatically map novel entity types to concepts, and adaptively recognize entities on-demand. We pre-train SDNet with large-scale corpus, and conduct experiments on 8 benchmarks from different domains. Experiments show that SDNet achieves competitive performances on all benchmarks and achieves the new state-of-the-art on 6 benchmarks, which demonstrates its effectiveness and robustness.

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Unified Structure Generation for Universal Information Extraction
Yaojie Lu | Qing Liu | Dai Dai | Xinyan Xiao | Hongyu Lin | Xianpei Han | Le Sun | Hua Wu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation framework, namely UIE, which can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Specifically, UIE uniformly encodes different extraction structures via a structured extraction language, adaptively generates target extractions via a schema-based prompt mechanism – structural schema instructor, and captures the common IE abilities via a large-scale pretrained text-to-structure model. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings for a wide range of entity, relation, event and sentiment extraction tasks and their unification. These results verified the effectiveness, universality, and transferability of UIE.

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Pre-training to Match for Unified Low-shot Relation Extraction
Fangchao Liu | Hongyu Lin | Xianpei Han | Boxi Cao | Le Sun
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Low-shot relation extraction (RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application. Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem to be with similar target but require totally different underlying abilities. In this paper, we propose Multi-Choice Matching Networks to unify low-shot relation extraction. To fill in the gap between zero-shot and few-shot RE, we propose the triplet-paraphrase meta-training, which leverages triplet paraphrase to pre-train zero-shot label matching ability and uses meta-learning paradigm to learn few-shot instance summarizing ability. Experimental results on three different low-shot RE tasks show that the proposed method outperforms strong baselines by a large margin, and achieve the best performance on few-shot RE leaderboard.

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Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View
Boxi Cao | Hongyu Lin | Xianpei Han | Fangchao Liu | Le Sun
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Prompt-based probing has been widely used in evaluating the abilities of pretrained language models (PLMs). Unfortunately, recent studies have discovered such an evaluation may be inaccurate, inconsistent and unreliable. Furthermore, the lack of understanding its inner workings, combined with its wide applicability, has the potential to lead to unforeseen risks for evaluating and applying PLMs in real-world applications. To discover, understand and quantify the risks, this paper investigates the prompt-based probing from a causal view, highlights three critical biases which could induce biased results and conclusions, and proposes to conduct debiasing via causal intervention. This paper provides valuable insights for the design of unbiased datasets, better probing frameworks and more reliable evaluations of pretrained language models. Furthermore, our conclusions also echo that we need to rethink the criteria for identifying better pretrained language models.

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Semantic-aware Contrastive Learning for More Accurate Semantic Parsing
Shan Wu | Chunlei Xin | Bo Chen | Xianpei Han | Le Sun
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Since the meaning representations are detailed and accurate annotations which express fine-grained sequence-level semtantics, it is usually hard to train discriminative semantic parsers via Maximum Likelihood Estimation (MLE) in an autoregressive fashion. In this paper, we propose a semantic-aware contrastive learning algorithm, which can learn to distinguish fine-grained meaning representations and take the overall sequence-level semantic into consideration. Specifically, a multi-level online sampling algorithm is proposed to sample confusing and diverse instances. Three semantic-aware similarity functions are designed to accurately measure the distance between meaning representations as a whole. And a ranked contrastive loss is proposed to pull the representations of the semantic-identical instances together and push negative instances away. Experiments on two standard datasets show that our approach achieves significant improvements over MLE baselines and gets state-of-the-art performances by simply applying semantic-aware contrastive learning on a vanilla Seq2Seq model.

2021

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Fine-grained Entity Typing via Label Reasoning
Qing Liu | Hongyu Lin | Xinyan Xiao | Xianpei Han | Le Sun | Hua Wu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.

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Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention
Jiawei Chen | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Event detection has long been troubled by the trigger curse: overfitting the trigger will harm the generalization ability while underfitting it will hurt the detection performance. This problem is even more severe in few-shot scenario. In this paper, we identify and solve the trigger curse problem in few-shot event detection (FSED) from a causal view. By formulating FSED with a structural causal model (SCM), we found that the trigger is a confounder of the context and the result, which makes previous FSED methods much easier to overfit triggers. To resolve this problem, we propose to intervene on the context via backdoor adjustment during training. Experiments show that our method significantly improves the FSED on both ACE05 and MAVEN datasets.

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Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion
Lingyong Yan | Xianpei Han | Le Sun
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Bootstrapping has become the mainstream method for entity set expansion. Conventional bootstrapping methods mostly define the expansion boundary using seed-based distance metrics, which heavily depend on the quality of selected seeds and are hard to be adjusted due to the extremely sparse supervision. In this paper, we propose BootstrapGAN, a new learning method for bootstrapping which jointly models the bootstrapping process and the boundary learning process in a GAN framework. Specifically, the expansion boundaries of different bootstrapping iterations are learned via different discriminator networks; the bootstrapping network is the generator to generate new positive entities, and the discriminator networks identify the expansion boundaries by trying to distinguish the generated entities from known positive entities. By iteratively performing the above adversarial learning, the generator and the discriminators can reinforce each other and be progressively refined along the whole bootstrapping process. Experiments show that BootstrapGAN achieves the new state-of-the-art entity set expansion performance.

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From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Jialong Tang | Hongyu Lin | Meng Liao | Yaojie Lu | Xianpei Han | Le Sun | Weijian Xie | Jin Xu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance and extrinsic experimental results verify the value of the extracted event relations.

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Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Boxi Cao | Hongyu Lin | Xianpei Han | Le Sun | Lingyong Yan | Meng Liao | Tong Xue | Jin Xu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.

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Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Yaojie Lu | Hongyu Lin | Jin Xu | Xianpei Han | Jialong Tang | Annan Li | Le Sun | Meng Liao | Shaoyi Chen
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.

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Element Intervention for Open Relation Extraction
Fangchao Liu | Lingyong Yan | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Open relation extraction aims to cluster relation instances referring to the same underlying relation, which is a critical step for general relation extraction. Current OpenRE models are commonly trained on the datasets generated from distant supervision, which often results in instability and makes the model easily collapsed. In this paper, we revisit the procedure of OpenRE from a causal view. By formulating OpenRE using a structural causal model, we identify that the above-mentioned problems stem from the spurious correlations from entities and context to the relation type. To address this issue, we conduct Element Intervention, which intervene on the context and entities respectively to obtain the underlying causal effects of them. We also provide two specific implementations of the interventions based on entity ranking and context contrasting. Experimental results on unsupervised relation extraction datasets show our method to outperform previous state-of-the-art methods and is robust across different datasets.

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De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention
Wenkai Zhang | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching. Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models. In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes. Based on the SCM, we learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we conduct backdoor adjustment to remove the spurious correlations introduced by the dictionary confounder. For inter-dictionary bias, we propose a causal invariance regularizer which will make DS-NER models more robust to the perturbation of dictionaries. Experiments on four datasets and three DS-NER models show that our method can significantly improve the performance of DS-NER.

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From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
Shan Wu | Bo Chen | Chunlei Xin | Xianpei Han | Le Sun | Weipeng Zhang | Jiansong Chen | Fan Yang | Xunliang Cai
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar-constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterancel and meaning representation. During synchronously decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve state-of-the-art unsupervised semantic parsing performance on multiple datasets.

2020

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A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land?
Hongyu Lin | Yaojie Lu | Jialong Tang | Xianpei Han | Le Sun | Zhicheng Wei | Nicholas Jing Yuan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Fine-tuning pretrained model has achieved promising performance on standard NER benchmarks. Generally, these benchmarks are blessed with strong name regularity, high mention coverage and sufficient context diversity. Unfortunately, when scaling NER to open situations, these advantages may no longer exist. And therefore it raises a critical question of whether previous creditable approaches can still work well when facing these challenges. As there is no currently available dataset to investigate this problem, this paper proposes to conduct randomization test on standard benchmarks. Specifically, we erase name regularity, mention coverage and context diversity respectively from the benchmarks, in order to explore their impact on the generalization ability of models. To further verify our conclusions, we also construct a new open NER dataset that focuses on entity types with weaker name regularity and lower mention coverage to verify our conclusion. From both randomization test and empirical experiments, we draw the conclusions that 1) name regularity is critical for the models to generalize to unseen mentions; 2) high mention coverage may undermine the model generalization ability and 3) context patterns may not require enormous data to capture when using pretrained encoders.

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ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling
Yaojie Lu | Annan Li | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the Fourteenth Workshop on Semantic Evaluation

ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence. This paper describes our system which is based on pretrained transformers. For the first subtask, we train several transformer-based classifiers for detecting counterfactual statements. For the second subtask, we formulate antecedent and consequence extraction as a query-based question answering problem. The two subsystems both achieved third place in the evaluation. Our system is openly released at https://github.com/casnlu/ISCASSemEval2020Task5.

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Syntactic and Semantic-driven Learning for Open Information Extraction
Jialong Tang | Yaojie Lu | Hongyu Lin | Xianpei Han | Le Sun | Xinyan Xiao | Hua Wu
Findings of the Association for Computational Linguistics: EMNLP 2020

One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem. In this paper, we propose a syntactic and semantic-driven learning approach, which can learn neural open IE models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervision. Specifically, we first employ syntactic patterns as data labelling functions and pretrain a base model using the generated labels. Then we propose a syntactic and semantic-driven reinforcement learning algorithm, which can effectively generalize the base model to open situations with high accuracy. Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model.

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Global Bootstrapping Neural Network for Entity Set Expansion
Lingyong Yan | Xianpei Han | Ben He | Le Sun
Findings of the Association for Computational Linguistics: EMNLP 2020

Bootstrapping for entity set expansion (ESE) has been studied for a long period, which expands new entities using only a few seed entities as supervision. Recent end-to-end bootstrapping approaches have shown their advantages in information capturing and bootstrapping process modeling. However, due to the sparse supervision problem, previous end-to-end methods often only leverage information from near neighborhoods (local semantics) rather than those propagated from the co-occurrence structure of the whole corpus (global semantics). To address this issue, this paper proposes Global Bootstrapping Network (GBN) with the “pre-training and fine-tuning” strategies for effective learning. Specifically, it contains a global-sighted encoder to capture and encode both local and global semantics into entity embedding, and an attention-guided decoder to sequentially expand new entities based on these embeddings. The experimental results show that the GBN learned by “pre-training and fine-tuning” strategies achieves state-of-the-art performance on two bootstrapping datasets.

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BERT-QE: Contextualized Query Expansion for Document Re-ranking
Zhi Zheng | Kai Hui | Ben He | Xianpei Han | Le Sun | Andrew Yates
Findings of the Association for Computational Linguistics: EMNLP 2020

Query expansion aims to mitigate the mismatch between the language used in a query and in a document. However, query expansion methods can suffer from introducing non-relevant information when expanding the query. To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion. In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models.

2019

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Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning
Yaojie Lu | Hongyu Lin | Xianpei Han | Le Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger words and generalization knowledge to detect unseen/sparse trigger words. Current neural event detection approaches focus on trigger-centric representations, which work well on distilling discrimination knowledge, but poorly on learning generalization knowledge. To address this problem, this paper proposes a Delta-learning approach to distill discrimination and generalization knowledge by effectively decoupling, incrementally learning and adaptively fusing event representation. Experiments show that our method significantly outperforms previous approaches on unseen/sparse trigger words, and achieves state-of-the-art performance on both ACE2005 and KBP2017 datasets.

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Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
Hongyu Lin | Yaojie Lu | Xianpei Han | Le Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recognizing nested entity mentions. In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i.e., although a mention can nest other mentions, they will not share the same head word. Specifically, we propose Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection. ARNs first identify anchor words (i.e., possible head words) of all mentions, and then recognize the mention boundaries for each anchor word by exploiting regular phrase structures. Furthermore, we also design Bag Loss, an objective function which can train ARNs in an end-to-end manner without using any anchor word annotation. Experiments show that ARNs achieve the state-of-the-art performance on three standard nested entity mention detection benchmarks.

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Cost-sensitive Regularization for Label Confusion-aware Event Detection
Hongyu Lin | Yaojie Lu | Xianpei Han | Le Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection.

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Learning to Bootstrap for Entity Set Expansion
Lingyong Yan | Xianpei Han | Le Sun | Ben He
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Bootstrapping for Entity Set Expansion (ESE) aims at iteratively acquiring new instances of a specific target category. Traditional bootstrapping methods often suffer from two problems: 1) delayed feedback, i.e., the pattern evaluation relies on both its direct extraction quality and extraction quality in later iterations. 2) sparse supervision, i.e., only few seed entities are used as the supervision. To address the above two problems, we propose a novel bootstrapping method combining the Monte Carlo Tree Search (MCTS) algorithm with a deep similarity network, which can efficiently estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. Experimental results confirm the effectiveness of the proposed method.

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Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition
Hongyu Lin | Yaojie Lu | Xianpei Han | Le Sun | Bin Dong | Shanshan Jiang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Current region-based NER models only rely on fully-annotated training data to learn effective region encoder, which often face the training data bottleneck. To alleviate this problem, this paper proposes Gazetteer-Enhanced Attentive Neural Networks, which can enhance region-based NER by learning name knowledge of entity mentions from easily-obtainable gazetteers, rather than only from fully-annotated data. Specially, we first propose an attentive neural network (ANN), which explicitly models the mention-context association and therefore is convenient for integrating externally-learned knowledge. Then we design an auxiliary gazetteer network, which can effectively encode name regularity of mentions only using gazetteers. Finally, the learned gazetteer network is incorporated into ANN for better NER. Experiments show that our ANN can achieve the state-of-the-art performance on ACE2005 named entity recognition benchmark. Besides, incorporating gazetteer network can further improve the performance and significantly reduce the requirement of training data.

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EUSP: An Easy-to-Use Semantic Parsing PlatForm
Bo An | Chen Bo | Xianpei Han | Le Sun
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.

2018

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Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing
Bo Chen | Bo An | Le Sun | Xianpei Han
Proceedings of the 27th International Conference on Computational Linguistics

Semantic parsers critically rely on accurate and high-coverage lexicons. However, traditional semantic parsers usually utilize annotated logical forms to learn the lexicon, which often suffer from the lexicon coverage problem. In this paper, we propose a graph-based semi-supervised learning framework that makes use of large text corpora and lexical resources. This framework first constructs a graph with a phrase similarity model learned by utilizing many text corpora and lexical resources. Next, graph propagation algorithm identifies the label distribution of unlabeled phrases from labeled ones. We evaluate our approach on two benchmarks: Webquestions and Free917. The results show that, in both datasets, our method achieves substantial improvement when comparing to the base system that does not utilize the learned lexicon, and gains competitive results when comparing to state-of-the-art systems.

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Model-Free Context-Aware Word Composition
Bo An | Xianpei Han | Le Sun
Proceedings of the 27th International Conference on Computational Linguistics

Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method.

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Accurate Text-Enhanced Knowledge Graph Representation Learning
Bo An | Bo Chen | Xianpei Han | Le Sun
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in different triples with the same representation, without considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowledge graph representation learning method, which can represent a relation/entity with different representations in different triples by exploiting additional textual information. Specifically, our method enhances representations by exploiting the entity descriptions and triple-specific relation mention. And a mutual attention mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge graph representation. Experimental results show that our method achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.

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Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing
Bo Chen | Le Sun | Xianpei Han
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper proposes a neural semantic parsing approach – Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on Overnight dataset and gets competitive performance on Geo and Atis datasets.

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Adaptive Scaling for Sparse Detection in Information Extraction
Hongyu Lin | Yaojie Lu | Xianpei Han | Le Sun
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often result in deficient performance of neural network based detection models. In this paper, we propose adaptive scaling, an algorithm which can handle the positive sparsity problem and directly optimize over F-measure via dynamic cost-sensitive learning. To this end, we borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring without introducing any additional hyper-parameters. Experiments show that our algorithm leads to a more effective and stable training of neural network based detection models.

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Nugget Proposal Networks for Chinese Event Detection
Hongyu Lin | Yaojie Lu | Xianpei Han | Le Sun
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can solve the word-trigger mismatch problem by directly proposing entire trigger nuggets centered at each character regardless of word boundaries. Specifically, NPNs perform event detection in a character-wise paradigm, where a hybrid representation for each character is first learned to capture both structural and semantic information from both characters and words. Then based on learned representations, trigger nuggets are proposed and categorized by exploiting character compositional structures of Chinese event triggers. Experiments on both ACE2005 and TAC KBP 2017 datasets show that NPNs significantly outperform the state-of-the-art methods.

2017

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Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
Hongyu Lin | Le Sun | Xianpei Han
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Reasoning with commonsense knowledge is critical for natural language understanding. Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multi-knowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.

2016

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ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features
Cheng Fu | Bo An | Xianpei Han | Le Sun
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Sentence Rewriting for Semantic Parsing
Bo Chen | Le Sun | Xianpei Han | Bo An
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Context-Sensitive Inference Rule Discovery: A Graph-Based Method
Xianpei Han | Le Sun
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Inference rule discovery aims to identify entailment relations between predicates, e.g., ‘X acquire Y –> X purchase Y’ and ‘X is author of Y –> X write Y’. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation—Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.

2015

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Graph-Based Collective Lexical Selection for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Shujian Huang | Xianpei Han | Junfeng Yao
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Context-Aware Topic Model for Statistical Machine Translation
Jinsong Su | Deyi Xiong | Yang Liu | Xianpei Han | Hongyu Lin | Junfeng Yao | Min Zhang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Learning to Mine Query Subtopics from Query Log
Zhenzhong Zhang | Le Sun | Xianpei Han
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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A Feature-Enriched Tree Kernel for Relation Extraction
Le Sun | Xianpei Han
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Semantic Consistency: A Local Subspace Based Method for Distant Supervised Relation Extraction
Xianpei Han | Le Sun
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Probabilistic Co-Bootstrapping Method for Entity Set Expansion
Bei Shi | Zhenzhong Zhang | Le Sun | Xianpei Han
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2012

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A Cascaded Approach for CIPS-SIGHAN Micro-Blog Word Segmentation Bakeoff 2012
Bei Shi | Xianpei Han | Le Sun
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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SIR-NERD: A Chinese Named Entity Recognition and Disambiguation System using a Two-Stage Method
Zehuan Peng | Le Sun | Xianpei Han
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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An Entity-Topic Model for Entity Linking
Xianpei Han | Le Sun
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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A Generative Entity-Mention Model for Linking Entities with Knowledge Base
Xianpei Han | Le Sun
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Structural Semantic Relatedness: A Knowledge-Based Method to Named Entity Disambiguation
Xianpei Han | Jun Zhao
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics