Sheng Xu


Improving Event Coreference Resolution Using Document-level and Topic-level Information
Sheng Xu | Peifeng Li | Qiaoming Zhu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Event coreference resolution (ECR) aims to cluster event mentions that refer to the same real-world events. Deep learning methods have achieved SOTA results on the ECR task. However, due to the encoding length limitation, previous methods either adopt classical pairwise models based on sentence-level context or split each document into multiple chunks and encode them separately. They failed to capture the interactions and contextual cues among those long-distance event mentions. Besides, high-level information, such as event topics, is rarely considered to enhance representation learning for ECR. To address the above two issues, we first apply a Longformer-based encoder to obtain the document-level embeddings and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context. In addition, we propose an event topic generator to infer the latent topic-level representations. Finally, using the above event embeddings, we employ a multiple tensor matching method to capture their interactions at the document, sentence, and topic levels. Experimental results on the KBP 2017 dataset show that our model outperforms the SOTA baselines.

DCT-Centered Temporal Relation Extraction
Liang Wang | Peifeng Li | Sheng Xu
Proceedings of the 29th International Conference on Computational Linguistics

Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). Moreover, DCT can act as a hub to semantically connect the other events and timexes in a document. Unfortunately, previous work cannot benefit from such critical information. To address the above issues, we propose a unified DCT-centered Temporal Relation Extraction model DTRE to identify the relations among events, timexes and DCT. Specifically, sentence-style DCT representation is introduced to address the first issue and unify event expressions, timexes and DCT. Then, a DCT-aware graph is applied to obtain their contextual structural representations. Furthermore, a DCT-anchoring multi-task learning framework is proposed to jointly predict three types of temporal relations in a batch. Finally, we apply a DCT-guided global inference to further enhance the global consistency among different relations. Experimental results on three datasets show that our DTRE outperforms several SOTA baselines on E-E, E-T and E-D significantly.


A Neural Local Coherence Analysis Model for Clarity Text Scoring
Panitan Muangkammuen | Sheng Xu | Fumiyo Fukumoto | Kanda Runapongsa Saikaew | Jiyi Li
Proceedings of the 28th International Conference on Computational Linguistics

Local coherence relation between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a text is well-structured or not. This paper follows the assumption and presents a method for scoring text clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target training data are also possible to discriminate well-structured of the target text and thus help to score the text clarity. We propose a text clarity scoring method that utilizes local coherence analysis with an out-domain setting, i.e. the training data for the source and target tasks are different from each other. The method with language model pre-training BERT firstly trains the local coherence model as an auxiliary manner and then re-trains it together with clarity text scoring model. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a single model, scoring text clarity model. Our source codes are available online.


Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
Sheng Xu | Peifeng Li | Fang Kong | Qiaoming Zhu | Guodong Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.


Building a Macro Chinese Discourse Treebank
Xiaomin Chu | Feng Jiang | Sheng Xu | Qiaoming Zhu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
Sheng Xu | Peifeng Li | Guodong Zhou | Qiaoming Zhu
Proceedings of the 27th International Conference on Computational Linguistics

The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.

MCDTB: A Macro-level Chinese Discourse TreeBank
Feng Jiang | Sheng Xu | Xiaomin Chu | Peifeng Li | Qiaoming Zhu | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Fol-lowing RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB.


Information Retrieval Oriented Word Segmentation based on Character Association Strength Ranking
Yixuan Liu | Bin Wang | Fan Ding | Sheng Xu
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing