Philip Bachman


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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.

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NewsQA: A Machine Comprehension Dataset
Adam Trischler | Tong Wang | Xingdi Yuan | Justin Harris | Alessandro Sordoni | Philip Bachman | Kaheer Suleman
Proceedings of the 2nd Workshop on Representation Learning for NLP

We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text in the articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. Analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (13.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available online.


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Natural Language Comprehension with the EpiReader
Adam Trischler | Zheng Ye | Xingdi Yuan | Philip Bachman | Alessandro Sordoni | Kaheer Suleman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data
Adam Trischler | Zheng Ye | Xingdi Yuan | Jing He | Philip Bachman
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)