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Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition.

, , and . ACL (1), page 476-485. The Association for Computer Linguistics, (2013)

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Hierarchical Intention Enhanced Network for Automatic Dialogue Coherence Assessment., , and . IJCNN, page 1-8. IEEE, (2019)Text Representations for Text Categorization: A Case Study in Biomedical Domain., , , and . IJCNN, page 2557-2562. IEEE, (2007)Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space., , , , , , and . CoRR, (2024)ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter., , and . SemEval@NAACL-HLT, page 256-261. The Association for Computer Linguistics, (2016)ECNU at SemEval-2020 Task 7: Assessing Humor in Edited News Headlines Using BiLSTM with Attention., , and . SemEval@COLING, page 995-1000. International Committee for Computational Linguistics, (2020)ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation., and . SemEval@COLING, page 265-270. The Association for Computer Linguistics, (2014)ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models., , and . SemEval@NAACL-HLT, page 231-235. Association for Computational Linguistics, (2018)ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task., , and . SemEval@NAACL-HLT, page 1048-1052. Association for Computational Linguistics, (2018)ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection., , and . SemEval@NAACL-HLT, page 999-1002. Association for Computational Linguistics, (2018)ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter., and . SemEval@NAACL-HLT, page 34-39. The Association for Computer Linguistics, (2015)