Saizheng Zhang


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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang | Peng Qi | Saizheng Zhang | Yoshua Bengio | William Cohen | Ruslan Salakhutdinov | Christopher D. Manning
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.

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Personalizing Dialogue Agents: I have a dog, do you have pets too?
Saizheng Zhang | Emily Dinan | Jack Urbanek | Arthur Szlam | Douwe Kiela | Jason Weston
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i)condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

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Neural Models for Key Phrase Extraction and Question Generation
Sandeep Subramanian | Tong Wang | Xingdi Yuan | Saizheng Zhang | Adam Trischler | Yoshua Bengio
Proceedings of the Workshop on Machine Reading for Question Answering

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.


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Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan | Tong Wang | Caglar Gulcehre | Alessandro Sordoni | Philip Bachman | Saizheng Zhang | Sandeep Subramanian | Adam Trischler
Proceedings of the 2nd Workshop on Representation Learning for NLP

We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.