未见语言对中的语码转换文本合成
Code-Switched Text Synthesis in Unseen Language Pairs
AI总结：
本文提出GLOSS，通过在冻结的多语言机器翻译模型上增加可学习的语码转换模块，并结合自训练算法，实现了在训练中未见过的语言对上进行语码转换文本合成，显著提升了BLEU和METEOR指标。
摘要：
现有的语码转换文本合成工作大多需要在目标语言对的语码转换文本上进行训练，这限制了模型在缺乏语码转换数据情况下的部署。本文研究了为训练数据中不存在的语言对合成语码转换文本的问题。我们提出了GLOSS，该模型构建在预训练多语言机器翻译模型（PMMTM）之上，并增加了一个语码转换模块。该模块可以是适配器或额外前缀，在训练期间从语码转换数据中学习语码转换模式，而GLOSS的主要组成部分，即PMMTM，保持冻结。仅调整语码转换模块的设计可防止模型对有限的语码转换训练数据过拟合。因此，GLOSS展现出在更广泛的语言对范围内泛化并合成语码转换文本的能力。此外，我们开发了一种针对目标语言对的自训练算法，以进一步增强GLOSS的可靠性。在四个语言对上的自动评估表明，与强基线相比，GLOSS在BLEU和METEOR分数上至少取得了55%的相对提升。在两个语言对上的人工评估进一步验证了GLOSS的成功。
英文摘要：
Existing efforts on text synthesis for code-switching mostly require training on code-switched texts in the target language pairs, limiting the deployment of the models to cases lacking code-switched data. In this work, we study the problem of synthesizing code-switched texts for language pairs absent from the training data. We introduce GLOSS, a model built on top of a pre-trained multilingual machine translation model (PMMTM) with an additional code-switching module. This module, either an adapter or extra prefixes, learns code-switching patterns from code-switched data during training, while the primary component of GLOSS, i.e., the PMMTM, is frozen. The design of only adjusting the code-switching module prevents our model from overfitting to the constrained training data for code-switching. Hence, GLOSS exhibits the ability to generalize and synthesize code-switched texts across a broader spectrum of language pairs. Additionally, we develop a self-training algorithm on target language pairs further to enhance the reliability of GLOSS. Automatic evaluations on four language pairs show that GLOSS achieves at least 55% relative BLEU and METEOR scores improvements compared to strong baselines. Human evaluations on two language pairs further validate the success of GLOSS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
