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 Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2017–2025
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.248
- DOI:
- Cite (ACL):
- Angrosh Mandya, James O’ Neill, Danushka Bollegala, and Frans Coenen. 2020. Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2017–2025, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering (Mandya et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.lrec-1.248.pdf