Does Context Help Mitigate Gender Bias in Neural Machine Translation?

Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution. Context-aware models have been previously suggested as a means to mitigate this type of bias. In this work, we examine this claim by analysing in detail the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish. Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias. These results highlight the need for more fine-grained approaches to bias mitigation in Neural Machine Translation.

Promoting Target Data in Context-aware Neural Machine Translation

Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts. Concatenation-based approaches in particular, still a strong baseline for document-level NMT, prepend source and/or target context sentences to the sentences to be translated, with model variants that exploit equal amounts of source and target data on each side achieving state-of-the-art results. In this work, we investigate whether target data should be further promoted within standard concatenation-based approaches, as most document-level phenomena rely on information that is present on the target language side. We evaluate novel concatenation-based variants where the target context is prepended to the source language, either in isolation or in combination with the source context. Experimental results in English-Russian and Basque-Spanish show that including target context in the source leads to large improvements on target language phenomena. On source-dependent phenomena, using only target language context in the source achieves parity with state-of-the-art concatenation approaches, or slightly underperforms, whereas combining source and target context on the source side leads to significant gains across the board.

Split and Rephrase with Large Language Models

The Split and Rephrase task, which consists in splitting complex sentences into a sequence of shorter grammatical sentences, while preserving the original meaning, can facilitate the processing of complex texts for humans and machines alike. In this work, we describe an approach based on large language models, which improves over the state of the art by large margins on all the major metrics for the task, on publicly available datasets. We also describe results from two human evaluations that further establish the significant improvements obtained with large language models and the viability of the approach. We evaluate different strategies, including fine-tuning pretrained language models of varying parameter size, and applying both zero-shot and few-shot in-context learning on instruction-tuned language models. Although the latter were markedly outperformed by fine-tuned models, they still achieved promising results overall. Our results thus demonstrate the strong potential of different variants of large language models for the Split and Rephrase task, using relatively small amounts of training samples and model parameters overall.