RankQA: Neural Question Answering with Answer Re-Ranking

Bernhard Kratzwald, Anna Eigenmann, Stefan Feuerriegel


Abstract
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer. However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused. In contrast, this work proposes RankQA: RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. The re-ranking leverages different features that are directly extracted from the QA pipeline, i.e., a combination of retrieval and comprehension features. While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves state-of-the-art performance on 3 out of 4 benchmark datasets. Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic. Here the answer re-ranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size. As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.
Anthology ID:
P19-1611
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6076–6085
Language:
URL:
https://aclanthology.org/P19-1611
DOI:
10.18653/v1/P19-1611
Bibkey:
Cite (ACL):
Bernhard Kratzwald, Anna Eigenmann, and Stefan Feuerriegel. 2019. RankQA: Neural Question Answering with Answer Re-Ranking. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6076–6085, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
RankQA: Neural Question Answering with Answer Re-Ranking (Kratzwald et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1611.pdf
Code
 bernhard2202/rankqa
Data
SQuADWebQuestionsWikiMovies