Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

This report provides a preliminary evaluation of ChatGPT for machine translation, including translation prompt, multilingual translation, and translation robustness. We adopt the prompts advised by ChatGPT to trigger its translation ability and find that the candidate prompts generally work well with minor performance differences. By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e.g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages. As for the translation robustness, ChatGPT does not perform as well as the commercial systems on biomedical abstracts or Reddit comments but exhibits good results on spoken language. Further, we explore an interesting strategy named $\mathbf{pivot~prompting}$ for distant languages, which asks ChatGPT to translate the source sentence into a high-resource pivot language before into the target language, improving the translation performance noticeably. With the launch of the GPT-4 engine, the translation performance of ChatGPT is significantly boosted, becoming comparable to commercial translation products, even for distant languages. Human analysis on Google Translate and ChatGPT suggests that ChatGPT with GPT-3.5 tends to generate more hallucinations and mis-translation errors while that with GPT-4 makes the least errors. In other words, ChatGPT has already become a good translator. Please refer to our Github project for more details: https://github.com/wxjiao/Is-ChatGPT-A-Good-TranslatorComment: Analyzed/compared the outputs between ChatGPT and Google Translate; both automatic and human evaluatio

The ethics of machine translation

In this paper I first describe the two main branches in machine translation research. I then go to discuss why the second of these, statistical machine translation, can cause some malaise among translation scholars. As some of the issues that arise are ethical in nature, I stop to ponder what an ethics of machine translation might involve, before considering the ethical stance adopted by some of the main protagonists in the development and popularisation of statistical machine translation, and in the teaching of translation

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge

Target-Side Context for Discriminative Models in Statistical Machine Translation

Discriminative translation models utilizing source context have been shown to help statistical machine translation performance. We propose a novel extension of this work using target context information. Surprisingly, we show that this model can be efficiently integrated directly in the decoding process. Our approach scales to large training data sizes and results in consistent improvements in translation quality on four language pairs. We also provide an analysis comparing the strengths of the baseline source-context model with our extended source-context and target-context model and we show that our extension allows us to better capture morphological coherence. Our work is freely available as part of Moses.Comment: Accepted as a long paper for ACL 201

Quantifying the effect of machine translation in a high-quality human translation production process

This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators' perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned