Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering

Angrosh Mandya, James O’ Neill, Danushka Bollegala, Frans Coenen


Abstract
The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph. Although existing approaches employ human-written ground-truth answers for answering conversational questions at test time, in a realistic scenario, the CoQA model will not have any access to ground-truth answers for the previous questions, compelling the model to rely upon its own previously predicted answers for answering the subsequent questions. In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems. To solve this problem, we propose a sampling strategy that dynamically selects between target answers and model predictions during training, thereby closely simulating the situation at test time. Further, we analyse the severity of this phenomena as a function of the question type, conversation length and domain type.
Anthology ID:
2020.lrec-1.248
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2017–2025
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.248
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.248.pdf