Neural Models for Key Phrase Extraction and Question Generation

Proceedings of the Workshop on Machine Reading for Question Answering, pages 78–88 Melbourne, Australia, July 19, 2018. c⃝2018 Association for Computational Linguistics 78 Neural Models for Key Phrase Extraction and Question Generation Sandeep Subramanian♠♣ Tong Wang♣ Xingdi Yuan♣ Saizheng Zhang♠ Yoshua Bengio ♠† Adam Trischler♣ ♣Microsoft Research, Montr´eal ♠MILA, Universit´e de Montr´eal †CIFAR Senior Fellow sandeep.subramanian.1@umontreal.ca Abstract We propose a two-stage neural model to tackle question generation from doc- uments. First, our model estimates the probability that word sequences in a doc- ument 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 cor- pus. Predicted key phrases then act as tar- get answers and condition a sequence-to- sequence question-generation model with a copy mechanism. Empirically, our key- phrase extraction model significantly out- performs an entity-tagging baseline and existing rule-based approaches. We fur- ther demonstrate that our question genera- tion 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 ma- chine reading systems or in educational settings. 1 Introduction Question answering and machine comprehension has gained increased interest in the past few years. An important contributing factor is the emergence of several large-scale QA datasets (Ra- jpurkar et al., 2016; Trischler et al., 2016; Nguyen et al., 2016; Joshi et al., 2017). However, the cre- ation of these datasets is a labour-intensive and ex- pensive process that usually comes at significant financial cost. Meanwhile, given the complexity of the problem space, even the largest QA dataset can still exhibit strong biases in many aspects in- cluding question and answer types, domain cover- age, linguistic style, etc. To address this limitation, we propose and eval- uate neural models for automatic question-answer pair generation that involves two inter-related components: first, a system to identify candidate answer entities or events (key phrases) within a passage or document (Becker et al., 2012); second, a question generation module to construct ques- tions about a given key phrases. As a financially more efficient and scalable alternative to the hu- man curation of QA datasets, the resulting system can potentially accelerate further progress in the field. Specifically, We formulate the key phrase ex- traction component as modeling the probability of potential answers conditioned on a given docu- ment, i.e., P(a|d). Inspired by successful work in question answering, we propose a sequence-to- sequence model that generates a set of key-phrase boundaries. This model can flexibly select an ar- bitrary number of key phrases from a document. To teach it to assign high probability to human- selected answers, we train the model on large- scale, crowd-sourced question-answering datasets. We thus take a purely data-driven approach to understand the priors that humans have when selecting answer candidates, working from the premise that crowdworkers tend to select enti- ties or events that interest them when formulat- ing their own comprehension questions. If this premise is correct, then the growing …

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