Yue Zhao


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Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang | Ao Wu | Jiaying Zhou | Yu Gu | Yue Zhao | Gong Cheng
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.


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Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
Yue Zhao | Xiaolong Jin | Yuanzhuo Wang | Xueqi Cheng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.


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Interactive Second Language Learning from News Websites
Tao Chen | Naijia Zheng | Yue Zhao | Muthu Kumar Chandrasekaran | Min-Yen Kan
Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications