CoVoSwitch：基于语调单位的合成代码混合文本的机器翻译
CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units
AI总结：
针对代码混合数据稀缺问题，提出基于语调单位合成代码混合文本的CoVoSwitch数据集，评估多语言模型翻译性能，发现混合翻译优于单语，且低资源语言译入英语获益最大。
摘要：
多语言代码混合研究常常因可用数据集的缺乏及其语言偏向性而受阻。为扩展语言表征，我们通过替换由PSST（一种从OpenAI的Whisper微调而来的语音分割模型）检测到的语调单位，并利用语音到文本翻译数据集CoVoST 2，合成了代码混合数据。借助我们的数据集CoVoSwitch（覆盖13种语言），我们评估了两个多语言翻译模型M2M-100 418M和NLLB-200 600M的代码混合翻译性能。我们发现，包含代码混合单元比单语设置带来更高的翻译性能，且模型在将代码混合翻译成英语时优于翻译成非英语。此外，低资源语言在翻译成英语时从代码混合单元的整合中获益最多，但在翻译成非英语时获益甚少。翻译成低资源语言的表现甚至不如原始的代码混合输入。我们发现，系统擅长复制英语词元，但在处理非英语词元时存在困难；单语设置中的脱靶问题在代码混合设置中同样存在；并且模型在代码混合翻译中会产生幻觉，引入在原始源句子中都不存在的词。CoVoSwitch及代码可在https://github.com/sophiayk20/covoswitch获取。
英文摘要：
Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. To expand language representation, we synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI's Whisper, using a speech-to-text translation dataset, CoVoST 2. With our dataset, CoVoSwitch, spanning 13 languages, we evaluate the code-switching translation performance of two multilingual translation models, M2M-100 418M and NLLB-200 600M. We reveal that the inclusion of code-switching units results in higher translation performance than monolingual settings and that models are better at code-switching translation into English than non-English. Further, low-resource languages gain most from integration of code-switched units when translating into English but much less when translating into non-English. Translations into low-resource languages also perform worse than even raw code-switched inputs. We find that systems excel at copying English tokens but struggle with non-English tokens, that the off-target problem in monolingual settings is also relevant in code-switching settings, and that models hallucinate in code-switching translation by introducing words absent in both of the original source sentences. CoVoSwitch and code are available at https://github.com/sophiayk20/covoswitch.
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。
