Xiaopeng Bai


2023

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A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis
Hongyi Wu | Xinshu Shen | Man Lan | Shaoguang Mao | Xiaopeng Bai | Yuanbin Wu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence. Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations. This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis. Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring. The dataset and related codes is available at https://github.com/cubenlp/CEDCC_corpus.

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Overview of CCL23-Eval Task 8: Chinese Essay Fluency Evaluation (CEFE) Task
Xinshu Shen | Hongyi Wu | Xiaopeng Bai | Yuanbin Wu | Aimin Zhou | Shaoguang Mao | Tao Ge | Yan Xia
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“This paper provides a comprehensive review of the CCL23-Eval Task 8, i.e., Chinese EssayFluency Evaluation (CEFE). The primary aim of this task is to systematically identify the typesof grammatical fine-grained errors that affect the readability and coherence of essays writtenby Chinese primary and secondary school students, and then to suggest suitable corrections toenhance the fluidity of their written expression. This task consists of three distinct tracks: (1)Coarse-grained and fine-grained error identification; (2) Character-level error identification andcorrection; (3) Error sentence rewriting. In the end, we received 44 completed registration forms,leading to a total of 130 submissions from 11 dedicated participating teams. We present theresults of all participants and our analysis of these results. Both the dataset and evaluation toolused in this task are available1.”

2017

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N-gram Model for Chinese Grammatical Error Diagnosis
Jianbo Zhao | Hao Liu | Zuyi Bao | Xiaopeng Bai | Si Li | Zhiqing Lin
Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA 2017)

Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis. This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors.

2015

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Chinese CogBank: Where to See the Cognitive Features of Chinese Words
Bin Li | Xiaopeng Bai | Siqi Yin | Jie Xu
Proceedings of the Third Workshop on Metaphor in NLP

2012

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Building a Chinese Lexical Taxonomy
Xiaopeng Bai | Nianwen Xue
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

2010

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Lexical Semantics-Syntactic Model for Defining and Subcategorizing Attribute Noun Class
Xiaopeng Bai | Hui Wang
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation