A Multi-Task Architecture on Relevance-based Neural Query Translation

Sheikh Muhammad Sarwar, Hamed Bonab, James Allan


Abstract
We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation. The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step. However, an NMT model trained on sentence-level parallel data is not aware of the vocabulary distribution of the retrieval corpus. We address this problem and propose a multi-task learning architecture that achieves 16% improvement over a strong baseline on Italian-English query-document dataset. We show using both quantitative and qualitative analysis that our model generates balanced and precise translations with the regularization effect it achieves from multi-task learning paradigm.
Anthology ID:
P19-1639
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6339–6344
Language:
URL:
https://aclanthology.org/P19-1639
DOI:
10.18653/v1/P19-1639
Bibkey:
Cite (ACL):
Sheikh Muhammad Sarwar, Hamed Bonab, and James Allan. 2019. A Multi-Task Architecture on Relevance-based Neural Query Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6339–6344, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Multi-Task Architecture on Relevance-based Neural Query Translation (Sarwar et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1639.pdf