Yunbo Cao


2023

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G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation
Yanzheng Xiang | Qian-Wen Zhang | Xu Zhang | Zejie Liu | Yunbo Cao | Deyu Zhou
Findings of the Association for Computational Linguistics: ACL 2023

We present a framework called G3R for complex and cross-domain Text-to-SQL generation. G3R aims to address two limitations of current approaches: (1) The structure of the abstract syntax tree (AST) is not fully explored during the decoding process which is crucial for complex SQL generation; (2) Domain knowledge is not incorporated to enhance their ability to generalise to unseen domains. G3R consists of a graph-guided SQL generator and a knowledge-enhanced re-ranking mechanism. Firstly, during the decoding process, An AST-Grammar bipartite graph is constructed for both the AST and corresponding grammar rules of the generated partial SQL query. The graph-guided SQL generator captures its structural information and fuses heterogeneous information to predict the action sequence which can construct the AST for the corresponding SQL query uniquely. Then, in the inference stage, a knowledge-enhanced re-ranking mechanism is proposed to introduce domain knowledge to re-rank candidate SQL queries from the beam output and choose the final answer. The SQL ranker is based on pre-trained language models (PLM) and contrastive learning with hybrid prompt tuning is incorporated to stimulate the knowledge of PLMs and make it more discriminative. The proposed approach achieves state-of-the-art results on the Spider and Spider-DK benchmarks, which are challenging complex and cross-domain benchmarks for Text-to-SQL semantic analysis.

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Enhancing Continual Relation Extraction via Classifier Decomposition
Heming Xia | Peiyi Wang | Tianyu Liu | Binghuai Lin | Yunbo Cao | Zhifang Sui
Findings of the Association for Computational Linguistics: ACL 2023

Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements have been shown by previous CRE studies, most of them only adopt a vanilla strategy when models first learn representations of new relations. In this work, we point out that there exist two typical biases after training of this vanilla strategy: classifier bias and representation bias, which causes the previous knowledge that the model learned to be shaded. To alleviate those biases, we propose a simple yet effective classifier decomposition framework that splits the last FFN layer into separated previous and current classifiers, so as to maintain previous knowledge and encourage the model to learn more robust representations at this training stage. Experimental results on two standard benchmarks show that our proposed framework consistently outperforms the state-of-the-art CRE models, which indicates that the importance of the first training stage to CRE models may be underestimated. Our code will be released upon acceptance.

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Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization
Shoujie Tong | Heming Xia | Damai Dai | Runxin Xu | Tianyu Liu | Binghuai Lin | Yunbo Cao | Zhifang Sui
Findings of the Association for Computational Linguistics: EMNLP 2023

Pretrained language models have achieved remarkable success in natural language understanding. However, fine-tuning pretrained models on limited training data tends to overfit and thus diminish performance. This paper presents Bi-Drop, a fine-tuning strategy that selectively updates model parameters using gradients from various sub-nets dynamically generated by dropout. The sub-net estimation of Bi-Drop is performed in an in-batch manner, so it overcomes the problem of hysteresis in sub-net updating, which is possessed by previous methods that perform asynchronous sub-net estimation. Also, Bi-Drop needs only one mini-batch to estimate the sub-net so it achieves higher utility of training data. Experiments on the GLUE benchmark demonstrate that Bi-Drop consistently outperforms previous fine-tuning methods. Furthermore, empirical results also show that Bi-Drop exhibits excellent generalization ability and robustness for domain transfer, data imbalance, and low-resource scenarios.

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DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
Xin Zheng | Tianyu Liu | Haoran Meng | Xu Wang | Yufan Jiang | Mengliang Rao | Binghuai Lin | Yunbo Cao | Zhifang Sui
Findings of the Association for Computational Linguistics: EMNLP 2023

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

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Denoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
Shaoxiang Wu | Damai Dai | Ziwei Qin | Tianyu Liu | Binghuai Lin | Yunbo Cao | Zhifang Sui
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. However, unlike other image-text multimodal tasks, video has longer multimodal sequences with more redundancy and noise in both visual and audio modalities. Prior denoising methods like forget gate are coarse in the granularity of noise filtering. They often suppress the redundant and noisy information at the risk of losing critical information. Therefore, we propose a denoising bottleneck fusion (DBF) model for fine-grained video multimodal fusion. On the one hand, we employ a bottleneck mechanism to filter out noise and redundancy with a restrained receptive field. On the other hand, we use a mutual information maximization module to regulate the filter-out module to preserve key information within different modalities. Our DBF model achieves significant improvement over current state-of-the-art baselines on multiple benchmarks covering multimodal sentiment analysis and multimodal summarization tasks. It proves that our model can effectively capture salient features from noisy and redundant video, audio, and text inputs. The code for this paper will be publicly available at https://github.com/WSXRHFG/DBF

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Soft Language Clustering for Multilingual Model Pre-training
Jiali Zeng | Yufan Jiang | Yongjing Yin | Yi Jing | Fandong Meng | Binghuai Lin | Yunbo Cao | Jie Zhou
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typologyfrom the source language or when pre-training data is limited in size. In this paper, we propose XLM-P, a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our space-efficient and model-agnostic XLM-P approach enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME, which include text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer.

2022

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Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems
Zhongli Li | Wenxuan Zhang | Chao Yan | Qingyu Zhou | Chao Li | Hongzhi Liu | Yunbo Cao
Findings of the Association for Computational Linguistics: ACL 2022

Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives. Recent work shows that existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. In this paper, we look at this issue and argue that the cause is a lack of overall understanding of MWP patterns. We first investigate how a neural network understands patterns only from semantics, and observe that, if the prototype equations are the same, most problems get closer representations and those representations apart from them or close to other prototypes tend to produce wrong solutions. Inspired by it, we propose a contrastive learning approach, where the neural network perceives the divergence of patterns. We collect contrastive examples by converting the prototype equation into a tree and seeking similar tree structures. The solving model is trained with an auxiliary objective on the collected examples, resulting in the representations of problems with similar prototypes being pulled closer. We conduct experiments on the Chinese dataset Math23k and the English dataset MathQA. Our method greatly improves the performance in monolingual and multilingual settings.

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The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking
Yinghui Li | Qingyu Zhou | Yangning Li | Zhongli Li | Ruiyang Liu | Rongyi Sun | Zizhen Wang | Chao Li | Yunbo Cao | Hai-Tao Zheng
Findings of the Association for Computational Linguistics: ACL 2022

Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors, which are mainly caused by the phonological or visual similarity. Recently, pre-trained language models (PLMs) promote the progress of CSC task. However, there exists a gap between the learned knowledge of PLMs and the goal of CSC task. PLMs focus on the semantics in text and tend to correct the erroneous characters to semantically proper or commonly used ones, but these aren’t the ground-truth corrections. To address this issue, we propose an Error-driven COntrastive Probability Optimization (ECOPO) framework for CSC task. ECOPO refines the knowledge representations of PLMs, and guides the model to avoid predicting these common characters through an error-driven way. Particularly, ECOPO is model-agnostic and it can be combined with existing CSC methods to achieve better performance. Extensive experiments and detailed analyses on SIGHAN datasets demonstrate that ECOPO is simple yet effective.

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Type-Driven Multi-Turn Corrections for Grammatical Error Correction
Shaopeng Lai | Qingyu Zhou | Jiali Zeng | Zhongli Li | Chao Li | Yunbo Cao | Jinsong Su
Findings of the Association for Computational Linguistics: ACL 2022

Grammatical Error Correction (GEC) aims to automatically detect and correct grammatical errors. In this aspect, dominant models are trained by one-iteration learning while performing multiple iterations of corrections during inference. Previous studies mainly focus on the data augmentation approach to combat the exposure bias, which suffers from two drawbacks. First, they simply mix additionally-constructed training instances and original ones to train models, which fails to help models be explicitly aware of the procedure of gradual corrections. Second, they ignore the interdependence between different types of corrections. In this paper, we propose a Type-Driven Multi-Turn Corrections approach for GEC. Using this approach, from each training instance, we additionally construct multiple training instances, each of which involves the correction of a specific type of errors. Then, we use these additionally-constructed training instances and the original one to train the model in turn. Experimental results and in-depth analysis show that our approach significantly benefits the model training. Particularly, our enhanced model achieves state-of-the-art single-model performance on English GEC benchmarks. We release our code at Github.

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Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Yinghui Li | Shirong Ma | Qingyu Zhou | Zhongli Li | Li Yangning | Shulin Huang | Ruiyang Liu | Chao Li | Yunbo Cao | Haitao Zheng
Findings of the Association for Computational Linguistics: EMNLP 2022

Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal information into CSC models to improve the performance. However, they overlook the rich knowledge in the dictionary, the reference book where one can learn how one character should be pronounced, written, and used. In this paper, we propose the LEAD framework, which renders the CSC model to learn heterogeneous knowledge from the dictionary in terms of phonetics, vision, and meaning. LEAD first constructs positive and negative samples according to the knowledge of character phonetics, glyphs, and definitions in the dictionary. Then a unified contrastive learning-based training scheme is employed to refine the representations of the CSC models. Extensive experiments and detailed analyses on the SIGHAN benchmark datasets demonstrate the effectiveness of our proposed methods.

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Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Shirong Ma | Yinghui Li | Rongyi Sun | Qingyu Zhou | Shulin Huang | Ding Zhang | Li Yangning | Ruiyang Liu | Zhongli Li | Yunbo Cao | Haitao Zheng | Ying Shen
Findings of the Association for Computational Linguistics: EMNLP 2022

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the development of CGEC research. However, there are two major limitations in the CGEC field: First, the lack of high-quality annotated training corpora prevents the performance of existing CGEC models from being significantly improved. Second, the grammatical errors in widely used test sets are not made by native Chinese speakers, resulting in a significant gap between the CGEC models and the real application. In this paper, we propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. Additionally, we present a challenging CGEC benchmark derived entirely from errors made by native Chinese speakers in real-world scenarios. Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.

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DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition
Jiali Zeng | Yufan Jiang | Yongjing Yin | Xu Wang | Binghuai Lin | Yunbo Cao
Findings of the Association for Computational Linguistics: EMNLP 2022

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a dual-teaching manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER.

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CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers
Dongling Xiao | LinZheng Chai | Qian-Wen Zhang | Zhao Yan | Zhoujun Li | Yunbo Cao
Findings of the Association for Computational Linguistics: EMNLP 2022

Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for multi-turn SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.

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DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
Haoran Meng | Zheng Xin | Tianyu Liu | Zizhen Wang | He Feng | Binghuai Lin | Xuemin Zhao | Yunbo Cao | Zhifang Sui
Findings of the Association for Computational Linguistics: EMNLP 2022

While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-intent sub-queries and then recovers all the coreferred and omitted information in the sub-queries. DialogUSR can serve as a plug-in and domain-agnostic module that empowers the multi-intent detection for the deployed chatbots with minimal efforts. We collect a high-quality naturally occurring dataset that covers 23 domains with a multi-step crowd-souring procedure. To benchmark the proposed dataset, we propose multiple action-based generative models that involve end-to-end and two-stage training, and conduct in-depth analyses on the pros and cons of the proposed baselines.

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Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Jiali Zeng | Yongjing Yin | Yufan Jiang | Shuangzhi Wu | Yunbo Cao
Findings of the Association for Computational Linguistics: EMNLP 2022

Contrastive learning has become a new paradigm for unsupervised sentence embeddings.Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.

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Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge
Linhai Zhang | Xuemeng Hu | Boyu Wang | Deyu Zhou | Qian-Wen Zhang | Yunbo Cao
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent years have witnessed growing interests in incorporating external knowledge such as pre-trained word embeddings (PWEs) or pre-trained language models (PLMs) into neural topic modeling. However, we found that employing PWEs and PLMs for topic modeling only achieved limited performance improvements but with huge computational overhead. In this paper, we propose a novel strategy to incorporate external knowledge into neural topic modeling where the neural topic model is pre-trained on a large corpus and then fine-tuned on the target dataset. Experiments have been conducted on three datasets and results show that the proposed approach significantly outperforms both current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs. Moreover, further study shows that the proposed approach greatly reduces the need for the huge size of training data.

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Hierarchical Curriculum Learning for AMR Parsing
Peiyi Wang | Liang Chen | Tianyu Liu | Damai Dai | Yunbo Cao | Baobao Chang | Zhifang Sui
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models. However, there exists a gap between their flat training objective (i.e., equally treats all output tokens) and the hierarchical AMR structure, which limits the model generalization. To bridge this gap, we propose a Hierarchical Curriculum Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula (IC). SC switches progressively from core to detail AMR semantic elements while IC transits from structure-simple to -complex AMR instances during training. Through these two warming-up processes, HCL reduces the difficulty of learning complex structures, thus the flat model can better adapt to the AMR hierarchy. Extensive experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations verify the effectiveness of HCL.

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AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications
Yusen Zhang | Zhongli Li | Qingyu Zhou | Ziyi Liu | Chao Li | Mina Ma | Yunbo Cao | Hongzhi Liu
Proceedings of the 29th International Conference on Computational Linguistics

To automatically correct handwritten assignments, the traditional approach is to use an OCR model to recognize characters and compare them to answers. The OCR model easily gets confused on recognizing handwritten Chinese characters, and the textual information of the answers is missing during the model inference. However, teachers always have these answers in mind to review and correct assignments. In this paper, we focus on the Chinese cloze tests correction and propose a multimodal approach(named AiM). The encoded representations of answers interact with the visual information of students’ handwriting. Instead of predicting ‘right’ or ‘wrong’, we perform the sequence labeling on the answer text to infer which answer character differs from the handwritten content in a fine-grained way. We take samples of OCR datasets as the positive samples for this task, and develop a negative sample augmentation method to scale up the training data. Experimental results show that AiM outperforms OCR-based methods by a large margin. Extensive studies demonstrate the effectiveness of our multimodal approach.

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An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Peiyi Wang | Runxin Xu | Tianyu Liu | Qingyu Zhou | Yunbo Cao | Baobao Chang | Zhifang Sui
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the noisy and nested tagging scenarios.

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G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
Shiwei Zhang | Yiyang Du | Guanzhong Liu | Zhao Yan | Yunbo Cao
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task. Unlike previous works which treat document-grounded dialogue modeling as a machine reading comprehension task from single document, MultiDoc2Dial task faces challenges of both seeking information from multiple documents and generating conversation response simultaneously. This paper summarizes our entries to agent response generation subtask in MultiDoc2Dial dataset. We propose a three-stage solution, Grounding-guided goal-oriented dialogues generation(G4), which predicts groundings from retrieved passages to guide the generation of the final response. Our experiments show that G4 achieves SacreBLEU score of 31.24 and F1 score of 44.6 which is 60.7% higher than the baseline model.

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HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
Zihan Wang | Peiyi Wang | Tianyu Liu | Binghuai Lin | Yunbo Cao | Zhifang Sui | Houfeng Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex label hierarchy. Recently, the pretrained language models (PLM)have been widely adopted in HTC through a fine-tuning paradigm. However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potential of PLMs cannot be fully tapped. To bridge the gap, in this paper, we propose HPT, a Hierarchy-aware Prompt Tuning method to handle HTC from a multi-label MLM perspective. Specifically, we construct a dynamic virtual template and label words that take the form of soft prompts to fuse the label hierarchy knowledge and introduce a zero-bounded multi-label cross-entropy loss to harmonize the objectives of HTC and MLM.Extensive experiments show HPT achieves state-of-the-art performances on 3 popular HTC datasets and is adept at handling the imbalance and low resource situations. Our code is available at https://github.com/wzh9969/HPT.

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Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation
Peiyi Wang | Yifan Song | Tianyu Liu | Binghuai Lin | Yunbo Cao | Sujian Li | Zhifang Sui
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Continual relation extraction (CRE) aims to continually learn new relations from a class-incremental data stream. CRE model usually suffers from catastrophic forgetting problem, i.e., the performance of old relations seriously degrades when the model learns new relations. Most previous work attributes catastrophic forgetting to the corruption of the learned representations as new relations come, with an implicit assumption that the CRE models have adequately learned the old relations. In this paper, through empirical studies we argue that this assumption may not hold, and an important reason for catastrophic forgetting is that the learned representations do not have good robustness against the appearance of analogous relations in the subsequent learning process. To address this issue, we encourage the model to learn more precise and robust representations through a simple yet effective adversarial class augmentation mechanism (ACA), which is easy to implement and model-agnostic. Experimental results show that ACA can consistently improve the performance of state-of-the-art CRE models on two popular benchmarks.

2021

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Improving BERT with Syntax-aware Local Attention
Zhongli Li | Qingyu Zhou | Chao Li | Ke Xu | Yunbo Cao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking
Heng-Da Xu | Zhongli Li | Qingyu Zhou | Chao Li | Zizhen Wang | Yunbo Cao | Heyan Huang | Xian-Ling Mao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning
Ximing Zhang | Qian-Wen Zhang | Zhao Yan | Ruifang Liu | Yunbo Cao
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Enhancing Dialogue-based Relation Extraction by Speaker and Trigger Words Prediction
Tianyang Zhao | Zhao Yan | Yunbo Cao | Zhoujun Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Diversity and Consistency: Exploring Visual Question-Answer Pair Generation
Sen Yang | Qingyu Zhou | Dawei Feng | Yang Liu | Chao Li | Yunbo Cao | Dongsheng Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Although showing promising values to downstream applications, generating question and answer together is under-explored. In this paper, we introduce a novel task that targets question-answer pair generation from visual images. It requires not only generating diverse question-answer pairs but also keeping the consistency of them. We study different generation paradigms for this task and propose three models: the pipeline model, the joint model, and the sequential model. We integrate variational inference into these models to achieve diversity and consistency. We also propose region representation scaling and attention alignment to improve the consistency further. We finally devise an evaluator as a quantitative metric for consistency. We validate our approach on two benchmarks, VQA2.0 and Visual-7w, by automatically and manually evaluating diversity and consistency. Experimental results show the effectiveness of our models: they can generate diverse or consistent pairs. Moreover, this task can be used to improve visual question generation and visual question answering.

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A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
Deyu Zhou | Yanzheng Xiang | Linhai Zhang | Chenchen Ye | Qian-Wen Zhang | Yunbo Cao
Findings of the Association for Computational Linguistics: EMNLP 2021

Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label. However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance. Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DC-MLMH) by decomposing it into head relation detection and conditional relation path generation. In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage. A majority-vote policy is employed to detect final KB answer. Comprehensive experiments were conducted on the FreebaseQA benchmark dataset. Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.

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Dialogue Response Selection with Hierarchical Curriculum Learning
Yixuan Su | Deng Cai | Qingyu Zhou | Zibo Lin | Simon Baker | Yunbo Cao | Shuming Shi | Nigel Collier | Yan Wang
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)

We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an “easy-to-difficult” scheme. Our learning framework consists of two complementary curricula: (1) corpus-level curriculum (CC); and (2) instance-level curriculum (IC). In CC, the model gradually increases its ability in finding the matching clues between the dialogue context and a response candidate. As for IC, it progressively strengthens the model’s ability in identifying the mismatching information between the dialogue context and a response candidate. Empirical studies on three benchmark datasets with three state-of-the-art matching models demonstrate that the proposed learning framework significantly improves the model performance across various evaluation metrics.

2020

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Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation
Chujie Zheng | Yunbo Cao | Daxin Jiang | Minlie Huang
Findings of the Association for Computational Linguistics: EMNLP 2020

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines.

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Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction
Tianyang Zhao | Zhao Yan | Yunbo Cao | Zhoujun Li
Proceedings of the 19th Chinese National Conference on Computational Linguistics

Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is efficient in extracting entities and relations simultaneously. However, the method is weak for its limited performance. In this paper, we propose several effective insights to address this problem. First, we propose an entity-specific Relative Position Representation (eRPR) to allow the model to fully leverage the distance information between entities and context tokens. Second, we introduce an auxiliary Global Relation Classification (GRC) to enhance the learning of local contextual features. Moreover, we improve the semantic representation by adopting a pre-trained language model BERT as the feature encoder. Finally, these new keypoints are closely integrated with the multi-head selection framework and optimized jointly. Extensive experiments on two benchmark datasets demonstrate that our approach overwhelmingly outperforms previous works in terms of all evaluation metrics, achieving significant improvements for relation F1 by +2.40% on CoNLL04 and +1.90% on ACE05, respectively.

2017

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A Statistical Framework for Product Description Generation
Jinpeng Wang | Yutai Hou | Jing Liu | Yunbo Cao | Chin-Yew Lin
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.

2014

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Collective Tweet Wikification based on Semi-supervised Graph Regularization
Hongzhao Huang | Yunbo Cao | Xiaojiang Huang | Heng Ji | Chin-Yew Lin
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Learning a Replacement Model for Query Segmentation with Consistency in Search Logs
Wei Zhang | Yunbo Cao | Chin-Yew Lin | Jian Su | Chew-Lim Tan
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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A Lazy Learning Model for Entity Linking using Query-Specific Information
Wei Zhang | Jian Su | Chew-Lim Tan | Yunbo Cao | Chin-Yew Lin
Proceedings of COLING 2012

2009

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A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums
Wen-Yun Yang | Yunbo Cao | Chin-Yew Lin
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Searching Questions by Identifying Question Topic and Question Focus
Huizhong Duan | Yunbo Cao | Chin-Yew Lin | Yong Yu
Proceedings of ACL-08: HLT

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A Probabilistic Model for Fine-Grained Expert Search
Shenghua Bao | Huizhong Duan | Qi Zhou | Miao Xiong | Yunbo Cao | Yong Yu
Proceedings of ACL-08: HLT

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Understanding and Summarizing Answers in Community-Based Question Answering Services
Yuanjie Liu | Shasha Li | Yunbo Cao | Chin-Yew Lin | Dingyi Han | Yong Yu
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Low-Quality Product Review Detection in Opinion Summarization
Jingjing Liu | Yunbo Cao | Chin-Yew Lin | Yalou Huang | Ming Zhou
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2003

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Uncertainty Reduction in Collaborative Bootstrapping: Measure and Algorithm
Yunbo Cao | Hang Li | Li Lian
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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Base Noun Phrase Translation Using Web Data and the EM Algorithm
Yunbo Cao | Hang Li
COLING 2002: The 19th International Conference on Computational Linguistics

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