ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g, low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this report, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose two (simple but effective) prompts: Task-Specific Prompts (TSP) and Domain-Specific Prompts (DSP). We show that: 1) The performance of ChatGPT depends largely on temperature, and a lower temperature usually can achieve better performance; 2) Emphasizing the task information further improves ChatGPT's performance, particularly in complex MT tasks; 3) Introducing domain information can elicit ChatGPT's generalization ability and improve its performance in the specific domain; 4) ChatGPT tends to generate hallucinations for non-English-centric MT tasks, which can be partially addressed by our proposed prompts but still need to be highlighted for the MT/NLP community. We also explore the effects of advanced in-context learning strategies and find a (negative but interesting) observation: the powerful chain-of-thought prompt leads to word-by-word translation behavior, thus bringing significant translation degradation.

/pdf/towards-making-the-most-of-chatgpt-for-machine-translation-1lwqmlyf.pdf

Towards Making the Most of ChatGPT for Machine Translation

Kaplan et al (1989) present an approach to machine translation based on co-description. In this paper we show that the notation is not as natural and expressive as it appears. We first show that the most natural analysis proposed in Kaplan et al (1989) cannot in fact cover the range of data for the important translational phenomenon in question. This contribution extends the work reported on in Sadler et al (1989) and Sadler et al (1990). We then go on to discuss alternatives which depart from or extend the formalism proposed in Kaplan et al (1989) in various respects, pointing out some directions for further research. The strategies discussed have been implemented.

/pdf/structural-non-correspondence-in-translation-50mjs68y1t.pdf

Structural non-correspondence in translation

This paper is a survey of the current machine translation research in the US, Europe and Japan. A short history of machine translation is presented first, followed by an overview of the current research work. Representative examples of a wide range of different approaches adopted by machine translation researchers are presented. These are described in detail along with a discussion of the practicalities of scaling up these approaches for operational environments. In support of this discussion, issues in, and techniques for, evaluating machine translation systems are addressed.

A Survey of Current Paradigms in Machine Translation

Publisher Summary This chapter presents a novel approach to Machine Translation (MT), called Shake-and-Bake translation, which is based on the idea of grammars as systems of constraints. Furthermore, in this approach translation equivalence is stipulated among bags of basic expressions of the languages concerned as presented in monolingual lexicalist grammars. Equivalence is constrained by equating variables in the logical forms of equivalent signs, providing a clear semantic basis for the notion of translation equivalence, and by any other constraints the bilingual lexicographer sees fit to include. Finally, the approach allows for the target language expressions to combine freely during generation, providing trivial accounts of phenomena for which structure-based approaches postulate complex transfer, noncompositional grammars, or undecidable logical inferences.

/pdf/shake-and-bake-translation-4lyvvve4g8.pdf

Shake-and-bake translation

Lexicalized Tree Adjoining Grammar (LTAG) is an attractive formalism for linguistic description mainly because of its extended domain of locality and its factoring recursion out from the domain of local dependencies (Joshi, 1985, Kroch and Joshi, 1985, Abeille, 1988). LTAG's extended domain of locality enables one to localize syntactic dependencies (such as filler-gap), as well as semantic dependencies (such as predicate-arguments). The aim of this paper is to show that these properties combined with the lexicalized property of LTAG are especially attractive for machine translation.The transfer between two languages, such as French and English, can be done by putting directly into correspondence large elementary units without going through some interlingual representation and without major changes to the source and target grammars. The underlying formalism for the transfer is "synchronous Tree Adjoining Grammars" (Shieber and Schabes [1990]). Transfer rules are stated as correspondences between nodes of trees of large domain of locality which are associated with words. We can thus define lexical transfer rules that avoid the defects of a mere word-to-word approach but still benefit from the simplicity and elegance of a lexical approach.We rely on the French and English LTAG grammars (Abeille [1988], Abeille [1990 (b)], Abeille et al. [1990], Abeille and Schabes [1989, 1990]) that have been designed over the past two years jointly at University of Pennsylvania and University of Paris 7-Jussieu.

/pdf/using-lexicalized-tags-for-machine-translation-rr4tio95qp.pdf

Using lexicalized tags for machine translation

We sketch and illustrate an approach to machine translation that exploits the potential of simultaneous correspondences between separate levels of linguistic representation, as formalized in the LFG notion of codescriptions The approach is illustrated with examples from English, German and French where the source and the target language sentence show noteworthy differences in linguistic analysis

Translation by structural correspondences

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Translation by structural correspondences