@inproceedings{diallo-etal-2019-learning,
title = "Learning Analogy-Preserving Sentence Embeddings for Answer Selection",
author = {Diallo, A{\"\i}ssatou and
Zopf, Markus and
F{\"u}rnkranz, Johannes},
editor = "Bansal, Mohit and
Villavicencio, Aline",
booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-1085",
doi = "10.18653/v1/K19-1085",
pages = "910--919",
abstract = "Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similarity-based technique.",
}
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%0 Conference Proceedings
%T Learning Analogy-Preserving Sentence Embeddings for Answer Selection
%A Diallo, Aïssatou
%A Zopf, Markus
%A Fürnkranz, Johannes
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F diallo-etal-2019-learning
%X Answer selection aims at identifying the correct answer for a given question from a set of potentially correct answers. Contrary to previous works, which typically focus on the semantic similarity between a question and its answer, our hypothesis is that question-answer pairs are often in analogical relation to each other. Using analogical inference as our use case, we propose a framework and a neural network architecture for learning dedicated sentence embeddings that preserve analogical properties in the semantic space. We evaluate the proposed method on benchmark datasets for answer selection and demonstrate that our sentence embeddings indeed capture analogical properties better than conventional embeddings, and that analogy-based question answering outperforms a comparable similarity-based technique.
%R 10.18653/v1/K19-1085
%U https://aclanthology.org/K19-1085
%U https://doi.org/10.18653/v1/K19-1085
%P 910-919
Markdown (Informal)
[Learning Analogy-Preserving Sentence Embeddings for Answer Selection](https://aclanthology.org/K19-1085) (Diallo et al., CoNLL 2019)
ACL