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

TL;DR

This paper introduces a pre-training approach for context-aware multi-encoder translation models that enhances training efficiency and translation quality by splitting sentence pairs to better utilize contextual information.

Contribution

It proposes a novel pre-training method using split sentence pairs to improve the training of contextual parameters in multi-encoder translation models.

Findings

Consistent BLEU score improvements across settings.

Enhanced learning of contextual parameters in low-resource scenarios.

Effective retrieval of relevant context segments.

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.