Yice Zhang


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HITSZ-HLT at SemEval-2022 Task 10: A Span-Relation Extraction Framework for Structured Sentiment Analysis
Yihui Li | Yifan Yang | Yice Zhang | Ruifeng Xu
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This paper describes our system that participated in the SemEval-2022 Task 10: Structured Sentiment Analysis, which aims to extract opinion tuples from texts.A full opinion tuple generally contains an opinion holder, an opinion target, the sentiment expression, and the corresponding polarity.The complex structure of the opinion tuple makes the task challenging.To address this task, we formalize it as a span-relation extraction problem and propose a two-stage extraction framework accordingly.In the first stage, we employ the span module to enumerate spans and then recognize the type of every span.In the second stage, we employ the relation module to determine the relation between spans.Our system achieves competitive results and ranks among the top-10 systems in almost subtasks.


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Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
Jianzhu Bao | Bin Liang | Jingyi Sun | Yice Zhang | Min Yang | Ruifeng Xu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.

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HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
Qinglin Zhu | Zijie Lin | Yice Zhang | Jingyi Sun | Xiang Li | Qihui Lin | Yixue Dang | Ruifeng Xu
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text’s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) problem and the Span Boundary Detection (SBD) problem separately and employ three state-of-the-art models. Next, we integrate predictions of these models to produce a more credible and complement result. Our system achieves a char-level score of 70.83%, ranking 1/91. In addition, we also explore the lexicon-based method, which is strongly interpretable and flexible in practice.


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CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples
Yice Zhang | Jiaxuan Lin | Yang Fan | Peng Jin | Yuanchao Liu | Bingquan Liu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.