Hurdles to Progress in Long-form Question Answering

Kalpesh Krishna, Aurko Roy, Mohit Iyyer


Abstract
The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the task formulation raises fundamental challenges regarding evaluation and dataset creation that currently preclude meaningful modeling progress. To demonstrate these challenges, we first design a new system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. While our system tops the public leaderboard, a detailed analysis reveals several troubling trends: (1) our system’s generated answers are not actually grounded in the documents that it retrieves; (2) ELI5 contains significant train / validation overlap, as at least 81% of ELI5 validation questions occur in paraphrased form in the training set; (3) ROUGE-L is not an informative metric of generated answer quality and can be easily gamed; and (4) human evaluations used for other text generation tasks are unreliable for LFQA. We offer suggestions to mitigate each of these issues, which we hope will lead to more rigorous LFQA research and meaningful progress in the future.
Anthology ID:
2021.naacl-main.393
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4940–4957
Language:
URL:
https://aclanthology.org/2021.naacl-main.393
DOI:
10.18653/v1/2021.naacl-main.393
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.393.pdf
Optional supplementary data:
 2021.naacl-main.393.OptionalSupplementaryData.zip
Code
 martiansideofthemoon/hurdles-longform-qa
Data
ELI5KILTNatural QuestionsPG-19