Predicting Perfect Quality Segments in MT Output with Fine-Tuned OpenAI LLM: Is it possible to capture editing distance patterns from historical data?
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Translation Quality Estimation (TQE) is an important step before deploying the output translation into usage. TQE is also critical in assessing machine translation (MT) and human translation (HT) quality without seeing the reference translations. In this work, we examine if the state-of-the-art large language models (LLMs) can be fine-tuned for the TQE task and their capability. We take ChatGPT as one example and approach TQE as a binary classification task. Using English to Italian, German, French, Japanese, Dutch, Portuguese, Turkish, and Chinese training corpora, our experimental results show that fine-tuned ChatGPT via its API can achieve a relatively high score on predicting translation quality, i.e. if the translation needs to be edited, but there is definitely much space to improve the accuracy. English-Italiano bilingual Abstract is available in the paper.
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