@inproceedings{rosso-mateus-etal-2020-deep,
title = "A Deep Metric Learning Method for Biomedical Passage Retrieval",
author = "Rosso-Mateus, Andr{\'e}s and
Gonz{\'a}lez, Fabio A. and
Montes-y-G{\'o}mez, Manuel",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.548",
doi = "10.18653/v1/2020.coling-main.548",
pages = "6229--6239",
abstract = "Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning problem, where we want to induce a metric between questions and passages that assign smaller distances to more relevant passages. In this work, we present a novel method for passage retrieval that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the training samples are composed of one anchor (the question) and two positive and negative samples (passages). However,and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of text and takes into consideration complementary relatedness measures. Besides, the paper presents a sampling strategy that selects both easy and hard negative samples which improves the accuracy of the trained model. The method is particularly well suited for domain-specific passage retrieval where it is very important to take into account different sources of information. The proposed approach was evaluated in a biomedical passage retrieval task, the BioASQ challenge, outperforming standard triplet loss substantially by 10{\%},and state-of-the-art performance by 26{\%}.",
}
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<abstract>Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning problem, where we want to induce a metric between questions and passages that assign smaller distances to more relevant passages. In this work, we present a novel method for passage retrieval that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the training samples are composed of one anchor (the question) and two positive and negative samples (passages). However,and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of text and takes into consideration complementary relatedness measures. Besides, the paper presents a sampling strategy that selects both easy and hard negative samples which improves the accuracy of the trained model. The method is particularly well suited for domain-specific passage retrieval where it is very important to take into account different sources of information. The proposed approach was evaluated in a biomedical passage retrieval task, the BioASQ challenge, outperforming standard triplet loss substantially by 10%,and state-of-the-art performance by 26%.</abstract>
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%0 Conference Proceedings
%T A Deep Metric Learning Method for Biomedical Passage Retrieval
%A Rosso-Mateus, Andrés
%A González, Fabio A.
%A Montes-y-Gómez, Manuel
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F rosso-mateus-etal-2020-deep
%X Passage retrieval is the task of identifying text snippets that are valid answers for a natural language posed question. One way to address this problem is to look at it as a metric learning problem, where we want to induce a metric between questions and passages that assign smaller distances to more relevant passages. In this work, we present a novel method for passage retrieval that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the training samples are composed of one anchor (the question) and two positive and negative samples (passages). However,and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of text and takes into consideration complementary relatedness measures. Besides, the paper presents a sampling strategy that selects both easy and hard negative samples which improves the accuracy of the trained model. The method is particularly well suited for domain-specific passage retrieval where it is very important to take into account different sources of information. The proposed approach was evaluated in a biomedical passage retrieval task, the BioASQ challenge, outperforming standard triplet loss substantially by 10%,and state-of-the-art performance by 26%.
%R 10.18653/v1/2020.coling-main.548
%U https://aclanthology.org/2020.coling-main.548
%U https://doi.org/10.18653/v1/2020.coling-main.548
%P 6229-6239
Markdown (Informal)
[A Deep Metric Learning Method for Biomedical Passage Retrieval](https://aclanthology.org/2020.coling-main.548) (Rosso-Mateus et al., COLING 2020)
ACL
- Andrés Rosso-Mateus, Fabio A. González, and Manuel Montes-y-Gómez. 2020. A Deep Metric Learning Method for Biomedical Passage Retrieval. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6229–6239, Barcelona, Spain (Online). International Committee on Computational Linguistics.