论文标题
学习通过学习交流来翻译
Learning to translate by learning to communicate
论文作者
论文摘要
我们制定并测试一种使用预先训练的多语言模型的新兴通信（EC）的技术，以改进现代无监督的NMT系统，尤其是对于低资源语言。有人认为，当前在纯文本中培训的NLP中，当前的主导范式不会产生强大的自然语言理解系统，并且需要对接地，面向目标和交互式语言学习的需求得到了很高的照明。在我们的方法中，我们将多种语言模型（Mbart，Liu等，2020）嵌入到EC图像引用游戏中，其中激励该模型使用多语言世代来完成视觉 - 基础的任务。假设是，这将使多种语言与共享的任务空间保持一致。我们介绍了EC微调的两种变体（Steinert-Threlkeld等，2022），其中一种在所研究的所有四种语言中都超过了仅反向翻译的基线，包括低资源语言尼泊尔语。
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.
