MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension

Xuanyu Zhang


Abstract
Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation. Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning. To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MCˆ2). We regard each conversation as a cube. 1D and 2D convolutions are integrated with RNN in our model. To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.
Anthology ID:
P19-1622
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6185–6190
Language:
URL:
https://aclanthology.org/P19-1622
DOI:
10.18653/v1/P19-1622
Bibkey:
Cite (ACL):
Xuanyu Zhang. 2019. MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6185–6190, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension (Zhang, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1622.pdf
Data
CoQA