papers AI Learner
The Github is limit! Click to go to the new site.

A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension

2019-05-30
Yasuhito Ohsugi, Itsumi Saito, Kyosuke Nishida, Hisako Asano, Junji Tomita

Abstract

Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pre-training language model, has been successful for single-turn machine comprehension, while modeling multiple turns of question answering with BERT has not been established because BERT has a limit on the number and the length of input sequences. In this paper, we propose a simple but effective method with BERT for CMC. Our method uses BERT to encode a paragraph independently conditioned with each question and each answer in a multi-turn context. Then, the method predicts an answer on the basis of the paragraph representations encoded with BERT. The experiments with representative CMC datasets, QuAC and CoQA, show that our method outperformed recently published methods (+0.8 F1 on QuAC and +2.1 F1 on CoQA). In addition, we conducted a detailed analysis of the effects of the number and types of dialogue history on the accuracy of CMC, and we found that the gold answer history, which may not be given in an actual conversation, contributed to the model performance most on both datasets.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1905.12848

PDF

http://arxiv.org/pdf/1905.12848


Comments

Content