FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih
Abstract
Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to shallow approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
BibTeX
@inproceedings{
huang2018flowqa,
title={Flow{QA}: Grasping Flow in History for Conversational Machine Comprehension},
author={Hsin-Yuan Huang and Eunsol Choi and Wen-tau Yih},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=ByftGnR9KX},
}