CNN for Text-Based Multiple Choice Question Answering

The task of Question Answering is at the very core of machine comprehension. In this paper, we propose a Convolutional Neural Network (CNN) model for text-based multiple choice question answering where questions are based on a particular article. Given an article and a multiple choice question, our model assigns a score to each question-option tuple and chooses the final option accordingly. We test our model on Textbook Question Answering (TQA) and SciQ dataset. Our model outperforms several LSTM-based baseline models on the two datasets.

[1]  Danqi Chen,et al.  A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task , 2016, ACL.

[2]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[3]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[4]  Rudolf Kadlec,et al.  Text Understanding with the Attention Sum Reader Network , 2016, ACL.

[5]  Philip Bachman,et al.  Natural Language Comprehension with the EpiReader , 2016, EMNLP.

[6]  Oren Kurland,et al.  Query Expansion Using Word Embeddings , 2016, CIKM.

[7]  Jonghyun Choi,et al.  Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ting Liu,et al.  Attention-over-Attention Neural Networks for Reading Comprehension , 2016, ACL.

[9]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[10]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[11]  Richard Socher,et al.  Dynamic Coattention Networks For Question Answering , 2016, ICLR.

[12]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[13]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[14]  Nelson F. Liu,et al.  Crowdsourcing Multiple Choice Science Questions , 2017, NUT@EMNLP.

[15]  Percy Liang,et al.  Compositional Semantic Parsing on Semi-Structured Tables , 2015, ACL.

[16]  Jason Weston,et al.  Memory Networks , 2014, ICLR.

[17]  Ali Farhadi,et al.  Bidirectional Attention Flow for Machine Comprehension , 2016, ICLR.

[18]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[19]  Yi Yang,et al.  WikiQA: A Challenge Dataset for Open-Domain Question Answering , 2015, EMNLP.