Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization

Edwin Simpson, Yang Gao, Iryna Gurevych


Abstract
For many NLP applications, such as question answering and summarization, the goal is to select the best solution from a large space of candidates to meet a particular user’s needs. To address the lack of user or task-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method uses Bayesian optimization to focus the user’s labeling effort on high quality candidates and integrate prior knowledge to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive multidocument summarization, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarization.
Anthology ID:
2020.tacl-1.49
Volume:
Transactions of the Association for Computational Linguistics, Volume 8
Month:
Year:
2020
Address:
Cambridge, MA
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
759–775
Language:
URL:
https://aclanthology.org/2020.tacl-1.49
DOI:
10.1162/tacl_a_00344
Bibkey:
Cite (ACL):
Edwin Simpson, Yang Gao, and Iryna Gurevych. 2020. Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization. Transactions of the Association for Computational Linguistics, 8:759–775.
Cite (Informal):
Interactive Text Ranking with Bayesian Optimization: A Case Study on Community QA and Summarization (Simpson et al., TACL 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.tacl-1.49.pdf