Cross-Pair Text Representations for Answer Sentence Selection

Kateryna Tymoshenko, Alessandro Moschitti


Abstract
High-level semantics tasks, e.g., paraphrasing, textual entailment or question answering, involve modeling of text pairs. Before the emergence of neural networks, this has been mostly performed using intra-pair features, which incorporate similarity scores or rewrite rules computed between the members within the same pair. In this paper, we compute scalar products between vectors representing similarity between members of different pairs, in place of simply using a single vector for each pair. This allows us to obtain a representation specific to any pair of pairs, which delivers the state of the art in answer sentence selection. Most importantly, our approach can outperform much more complex algorithms based on neural networks.
Anthology ID:
D18-1240
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2162–2173
Language:
URL:
https://www.aclweb.org/anthology/D18-1240
DOI:
10.18653/v1/D18-1240
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1240.pdf