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- Caiming Xiong, Victor Zhong, Richard Socher
- ICLR
- 2016
Several deep learning models have been proposed for question answering. How- ever, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers.… (More)
- Victor Zhong, Caiming Xiong, Richard Socher
- ArXiv
- 2018
Relational databases store a significant amount of the worlds data. However, accessing this data currently requires users to understand a query language such as SQL. We propose Seq2SQL, a deep neural… (More)
- Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, Christopher D. Manning
- EMNLP
- 2017
Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents… (More)
- Caiming Xiong, Victor Zhong, Richard Socher
- ICLR
- 2017
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally… (More)
- Sewon Min, Victor Zhong, Richard Socher, Caiming Xiong
- ACL
- 2018
Neural models for question answering (QA) over documents have achieved significant performance improvements. Although effective, these models do not scale to large corpora due to their complex… (More)
- Gabor Angeli, Victor Zhong, +6 authors Christopher D. Manning
- TAC
- 2015
A central challenge in relation extraction is the lack of supervised training data. Pattern-based relation extractors suffer from low recall, whereas distant supervision yields noisy data which hurts… (More)
- Victor Zhong, Caiming Xiong, Richard Socher
- ArXiv
- 2018
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally… (More)
- Victor Zhong, Caiming Xiong, Richard Socher
- ACL
- 2018
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a… (More)
- Victor Zhong, Luke S. Zettlemoyer
- ACL
- 2019
Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the… (More)