@inproceedings{zhang-etal-2019-improving-low,
title = "Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations",
author = "Zhang, Rui and
Westerfield, Caitlin and
Shim, Sungrok and
Bingham, Garrett and
Fabbri, Alexander and
Hu, William and
Verma, Neha and
Radev, Dragomir",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1306",
doi = "10.18653/v1/P19-1306",
pages = "3173--3179",
abstract = "In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the Material dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.",
}
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<abstract>In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the Material dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.</abstract>
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%0 Conference Proceedings
%T Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
%A Zhang, Rui
%A Westerfield, Caitlin
%A Shim, Sungrok
%A Bingham, Garrett
%A Fabbri, Alexander
%A Hu, William
%A Verma, Neha
%A Radev, Dragomir
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-etal-2019-improving-low
%X In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the Material dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.
%R 10.18653/v1/P19-1306
%U https://aclanthology.org/P19-1306
%U https://doi.org/10.18653/v1/P19-1306
%P 3173-3179
Markdown (Informal)
[Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations](https://aclanthology.org/P19-1306) (Zhang et al., ACL 2019)
ACL
- Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, William Hu, Neha Verma, and Dragomir Radev. 2019. Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3173–3179, Florence, Italy. Association for Computational Linguistics.