Rethinking the Objectives of Extractive Question Answering
August 28, 2020 ยท Declared Dead ยท ๐ Workshop on Machine Reading for Question Answering
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Authors
Martin Fajcik, Josef Jon, Pavel Smrz
arXiv ID
2008.12804
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.LG
Citations
12
Venue
Workshop on Machine Reading for Question Answering
Last Checked
4 months ago
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.
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