Xinchi Chen


2023

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Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
Jifan Chen | Yuhao Zhang | Lan Liu | Rui Dong | Xinchi Chen | Patrick Ng | William Yang Wang | Zhiheng Huang
Findings of the Association for Computational Linguistics: ACL 2023

There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).However, existing methods typically encode task information with a simple dataset name as a prefix to the encoder. This not only limits the effectiveness of multi-task learning, but also hinders the model’s ability to generalize to new domains or tasks that were not seen during training, which is crucial for real-world applications. In this paper, we propose compositional task configurations, a set of prompts prepended to the encoder to improve cross-task generalization of unified models. We design the task configurations to explicitly specify the task type, as well as its input and output types. We show that this not only allows the model to better learn shared knowledge across different tasks at training, but also allows us to control the model by composing new configurations that apply novel input-output combinations in a zero-shot manner. We demonstrate via experiments over ten table-to-text tasks that our method outperforms the UnifiedSKG baseline by noticeable margins in both in-domain and zero-shot settings, with average improvements of +0.5 and +12.6 from using a T5-large backbone, respectively.

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Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Xiyang Hu | Xinchi Chen | Peng Qi | Deguang Kong | Kunlun Liu | William Yang Wang | Zhiheng Huang
Findings of the Association for Computational Linguistics: ACL 2023

Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora between English and other languages are available. We leverage parallel and non-parallel corpora to improve the pretrained multilingual language models’ cross-lingual transfer ability. We design a semantic contrastive loss to align representations of parallel sentences that share the same semantics in different languages, and a new language contrastive loss to leverage parallel sentence pairs to remove language-specific information in sentence representations from non-parallel corpora. When trained on English IR data with these losses and evaluated zero-shot on non-English data, our model demonstrates significant improvement to prior work on retrieval performance, while it requires much less computational effort. We also demonstrate the value of our model for a practical setting when a parallel corpus is only available for a few languages, but a lack of parallel corpora resources persists for many other low-resource languages. Our model can work well even with a small number of parallel sentences, and be used as an add-on module to any backbones and other tasks.

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Hybrid Hierarchical Retrieval for Open-Domain Question Answering
Manoj Ghuhan Arivazhagan | Lan Liu | Peng Qi | Xinchi Chen | William Yang Wang | Zhiheng Huang
Findings of the Association for Computational Linguistics: ACL 2023

Retrieval accuracy is crucial to the performance of open-domain question answering (ODQA) systems. Recent work has demonstrated that dense hierarchical retrieval (DHR), which retrieves document candidates first and then relevant passages from the refined document set, can significantly outperform the single stage dense passage retriever (DPR). While effective, this approach requires document structure information to learn document representation and is hard to adopt to other domains without this information. Additionally, the dense retrievers tend to generalize poorly on out-of-domain data comparing with sparse retrievers such as BM25. In this paper, we propose Hybrid Hierarchical Retrieval (HHR) to address the existing limitations. Instead of relying solely on dense retrievers, we can apply sparse retriever, dense retriever, and a combination of them in both stages of document and passage retrieval. We perform extensive experiments on ODQA benchmarks and observe that our framework not only brings in-domain gains, but also generalizes better to zero-shot TriviaQA and Web Questions datasets with an average of 4.69% improvement on recall@100 over DHR. We also offer practical insights to trade off between retrieval accuracy, latency, and storage cost. The code is available on github.

2022

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Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Neves Ribeiro | Shen Wang | Xiaofei Ma | Rui Dong | Xiaokai Wei | Henghui Zhu | Xinchi Chen | Peng Xu | Zhiheng Huang | Andrew Arnold | Dan Roth
Findings of the Association for Computational Linguistics: NAACL 2022

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system’s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.

2021

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Contrastive Document Representation Learning with Graph Attention Networks
Peng Xu | Xinchi Chen | Xiaofei Ma | Zhiheng Huang | Bing Xiang
Findings of the Association for Computational Linguistics: EMNLP 2021

Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention complexity, most of the pretrained Transformers models can only handle relatively short text. It is still a challenge when it comes to modeling very long documents. In this work, we propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. This graph attention network allows us to leverage the high-level semantic structure of the document. In addition, based on our graph document model, we design a simple contrastive learning strategy to pretrain our models on a large amount of unlabeled corpus. Empirically, we demonstrate the effectiveness of our approaches in document classification and document retrieval tasks.

2019

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Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
Xinchi Chen | Chunchuan Lyu | Ivan Titov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks (Sabour et al., 2017): each proposition is encoded as a tuple of capsules, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the non-refinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and context-sensitive way.

2018

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Convolutional Interaction Network for Natural Language Inference
Jingjing Gong | Xipeng Qiu | Xinchi Chen | Dong Liang | Xuanjing Huang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Attention-based neural models have achieved great success in natural language inference (NLI). In this paper, we propose the Convolutional Interaction Network (CIN), a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. Specifically, CIN encodes one sentence with the filters dynamically generated based on another sentence. Since the filters may be designed to have various numbers and sizes, CIN can capture more complicated interaction patterns. Experiments on three large datasets demonstrate CIN’s efficacy.

2017

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Adversarial Multi-Criteria Learning for Chinese Word Segmentation
Xinchi Chen | Zhan Shi | Xipeng Qiu | Xuanjing Huang
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.

2015

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Sentence Modeling with Gated Recursive Neural Network
Xinchi Chen | Xipeng Qiu | Chenxi Zhu | Shiyu Wu | Xuanjing Huang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Long Short-Term Memory Neural Networks for Chinese Word Segmentation
Xinchi Chen | Xipeng Qiu | Chenxi Zhu | Pengfei Liu | Xuanjing Huang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Transition-based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks
Xinchi Chen | Yaqian Zhou | Chenxi Zhu | Xipeng Qiu | Xuanjing Huang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Multi-Timescale Long Short-Term Memory Neural Network for Modelling Sentences and Documents
Pengfei Liu | Xipeng Qiu | Xinchi Chen | Shiyu Wu | Xuanjing Huang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
Chenxi Zhu | Xipeng Qiu | Xinchi Chen | Xuanjing Huang
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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Gated Recursive Neural Network for Chinese Word Segmentation
Xinchi Chen | Xipeng Qiu | Chenxi Zhu | Xuanjing Huang
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)