Jingming Liu, Yumeng Li, Wei Shi, Yao-Xiang Ding, Hui Su, Kun Zhou
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2026
We propose a reinforcement learning approach for language-model-based information retrieval through query-document co-augmentation.
Jingming Liu, Yumeng Li, Wei Shi, Yao-Xiang Ding, Hui Su, Kun Zhou
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2026
We propose a reinforcement learning approach for language-model-based information retrieval through query-document co-augmentation.
Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP) 2021
We propose an entity-enhanced model for implicit discourse relation classification in the biomedical domain, leveraging entity information to improve relation detection.
Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP) 2021
We propose an entity-enhanced model for implicit discourse relation classification in the biomedical domain, leveraging entity information to improve relation detection.
PhD Thesis, Saarland University 2020
This dissertation addresses the data bottleneck problem in implicit discourse relation classification through cross-lingual and transfer learning approaches.
PhD Thesis, Saarland University 2020
This dissertation addresses the data bottleneck problem in implicit discourse relation classification through cross-lingual and transfer learning approaches.
Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019
We show that next sentence prediction, as used in BERT pre-training, can effectively help implicit discourse relation classification both within and across domains.
Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019
We show that next sentence prediction, as used in BERT pre-training, can effectively help implicit discourse relation classification both within and across domains.
Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, Asad Sayeed
Workshop on Narrative Understanding (StoryNLP@ACL) 2019
We propose a hybrid model that combines a language model with a discourse-level planning component to generate globally coherent stories.
Fangzhou Zhai, Vera Demberg, Pavel Shkadzko, Wei Shi, Asad Sayeed
Workshop on Narrative Understanding (StoryNLP@ACL) 2019
We propose a hybrid model that combines a language model with a discourse-level planning component to generate globally coherent stories.
Wei Shi, Frances Yung, Vera Demberg
Workshop on Discourse Relation Parsing and Treebanking (DISRPT@NAACL) 2019
We explore cross-lingual explicitation as a method for acquiring annotated data for implicit discourse relation classification.
Wei Shi, Frances Yung, Vera Demberg
Workshop on Discourse Relation Parsing and Treebanking (DISRPT@NAACL) 2019
We explore cross-lingual explicitation as a method for acquiring annotated data for implicit discourse relation classification.
13th International Conference on Computational Semantics (IWCS) 2019
We propose a Seq2Seq approach to explicitate implicit discourse connectives, and use the generated connectives to improve implicit discourse relation classification.
13th International Conference on Computational Semantics (IWCS) 2019
We propose a Seq2Seq approach to explicitate implicit discourse connectives, and use the generated connectives to improve implicit discourse relation classification.
Katharina Spalek, Birte Bergmann, Wei Shi, Vera Demberg
Discourse Expectations: Theoretical, Experimental, and Computational Perspectives 2019
We investigate how the focus particle 'only' influences expectations for causal coherence relations in discourse processing.
Katharina Spalek, Birte Bergmann, Wei Shi, Vera Demberg
Discourse Expectations: Theoretical, Experimental, and Computational Perspectives 2019
We investigate how the focus particle 'only' influences expectations for causal coherence relations in discourse processing.
Wei Shi, Frances Yung, Raphael Rubino, Vera Demberg
8th International Joint Conference on Natural Language Processing (IJCNLP) 2017
We leverage explicit discourse connectives that appear in translations to obtain additional training signal for implicit discourse relation classification.
Wei Shi, Frances Yung, Raphael Rubino, Vera Demberg
8th International Joint Conference on Natural Language Processing (IJCNLP) 2017
We leverage explicit discourse connectives that appear in translations to obtain additional training signal for implicit discourse relation classification.
15th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 2017
We demonstrate the need for cross-validation in discourse relation classification, showing that standard train/test splits can lead to unreliable evaluation.
15th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 2017
We demonstrate the need for cross-validation in discourse relation classification, showing that standard train/test splits can lead to unreliable evaluation.
Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, Bo Xu
54th Annual Meeting of the Association for Computational Linguistics (ACL) 2016
We propose an attention-based bidirectional LSTM model for relation classification, which can capture the most important semantic information in a sentence.
Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, Bo Xu
54th Annual Meeting of the Association for Computational Linguistics (ACL) 2016
We propose an attention-based bidirectional LSTM model for relation classification, which can capture the most important semantic information in a sentence.