Learning to translate by learning to communicate

• URL: http://arxiv.org/abs/2207.07025v2
• Date: Thu, 19 Oct 2023 17:35:01 GMT
• Title: Learning to translate by learning to communicate
• Authors: C.M. Downey, Xuhui Zhou, Leo Z. Liu, Shane Steinert-Threlkeld
• Abstract summary: We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems. In our approach, we embed a multilingual model into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et al., 2022), one of which outperforms a backtranslation-only baseline in all four languages investigated.
• Score: 11.43638897327485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems, especially for low-resource languages. It has been argued that the current dominant paradigm in NLP of pre-training on text-only corpora will not yield robust natural language understanding systems, and the need for grounded, goal-oriented, and interactive language learning has been high lighted. In our approach, we embed a multilingual model (mBART, Liu et al., 2020) into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task. The hypothesis is that this will align multiple languages to a shared task space. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et al., 2022), one of which outperforms a backtranslation-only baseline in all four languages investigated, including the low-resource language Nepali.

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