Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models

Lorenzo Lupo, Marco Dinarelli, Laurent Besacier


Abstract
Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence. The context encoding is undertaken by contextual parameters, trained on document-level data. In this work, we discuss the difficulty of training these parameters effectively, due to the sparsity of the words in need of context (i.e., the training signal), and their relevant context. We propose to pre-train the contextual parameters over split sentence pairs, which makes an efficient use of the available data for two reasons. Firstly, it increases the contextual training signal by breaking intra-sentential syntactic relations, and thus pushing the model to search the context for disambiguating clues more frequently. Secondly, it eases the retrieval of relevant context, since context segments become shorter. We propose four different splitting methods, and evaluate our approach with BLEU and contrastive test sets. Results show that it consistently improves learning of contextual parameters, both in low and high resource settings.
Anthology ID:
2022.acl-long.312
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4557–4572
Language:
URL:
https://aclanthology.org/2022.acl-long.312
DOI:
10.18653/v1/2022.acl-long.312
Bibkey:
Cite (ACL):
Lorenzo Lupo, Marco Dinarelli, and Laurent Besacier. 2022. Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4557–4572, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models (Lupo et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.312.pdf
Software:
 2022.acl-long.312.software.zip
Code
 lorelupo/divide-and-rule
Data
IWSLT 2017OpenSubtitlesWMT 2014