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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1639.pdf