Rethinking the Objectives of Extractive Question Answering

Martin Fajcik, Josef Jon, Pavel Smrz


Abstract
This work demonstrates that using the objective with independence assumption for modelling the span probability P (a_s , a_e ) = P (a_s )P (a_e) of span starting at position a_s and ending at position a_e has adverse effects. Therefore we propose multiple approaches to modelling joint probability P (a_s , a_e) directly. Among those, we propose a compound objective, composed from the joint probability while still keeping the objective with independence assumption as an auxiliary objective. We find that the compound objective is consistently superior or equal to other assumptions in exact match. Additionally, we identified common errors caused by the assumption of independence and manually checked the counterpart predictions, demonstrating the impact of the compound objective on the real examples. Our findings are supported via experiments with three extractive QA models (BIDAF, BERT, ALBERT) over six datasets and our code, individual results and manual analysis are available online.
Anthology ID:
2021.mrqa-1.2
Volume:
Proceedings of the 3rd Workshop on Machine Reading for Question Answering
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
MRQA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–27
Language:
URL:
https://aclanthology.org/2021.mrqa-1.2
DOI:
10.18653/v1/2021.mrqa-1.2
Bibkey:
Cite (ACL):
Martin Fajcik, Josef Jon, and Pavel Smrz. 2021. Rethinking the Objectives of Extractive Question Answering. In Proceedings of the 3rd Workshop on Machine Reading for Question Answering, pages 14–27, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Rethinking the Objectives of Extractive Question Answering (Fajcik et al., MRQA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.mrqa-1.2.pdf
Code
 KNOT-FIT-BUT/JointSpanExtraction
Data
MRQANatural QuestionsNewsQASQuADTriviaQA