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Meng, Ji and Oakes, Michael, eds. (2019): Advances in Empirical Translation Studies: Developing Translation Resources and Technologies. Cambridge: Cambridge University Press, 270 p.

  • Elliott Macklovitch

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  • Elliott Macklovitch
    Université de Montréal, Montréal, Canada

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Couverture de New Contexts in Discourse Analysis for Translation and Interpretation, Volume 65, numéro 1, avril 2020, p. 1-285, Meta

Let me begin by laying my cards on the table, briefly informing readers of my background and the biases that inevitably come with it. Trained as a linguist in the old Chomskyan school of generative linguistics, I joined the machine translation project at the Université de Montréal in 1977. From that point on, almost my entire professional life has been spent in research and development, both in MT and machine-aided translation (MAT), except for the few years when I earned my living as a French-to-English translator at the Canadian federal Translation Bureau. Today, I work as an independent consultant in machine translation, while continuing to translate, both for pay and for pleasure. Hence, readers will not be too surprised to learn that the chapters that I found most interesting in this collection of articles are those that deal with MT and MAT. Chief among these are two articles by Mark Seligman, one on the evolving treatment of semantics in MT, the other, co-authored with Alex Waibel, on speech-to-speech translation. The first article is a lengthy and impressive historical overview of the role that semantics has (and has not) played in MT. Seligman opens on a philosophical note, picking up John Searle’s well-known Chinese room argument in which Searle contends that no computer program (not just MT) can ever operate with anything like a human understanding of the language it processes; all it can do is manipulate symbols. Seligman grudgingly accepts Searle’s general point, but only for those programs that operate without any explicit meaning representations. He then goes on to trace the role of semantics throughout MT’s long 70-year history, from which we learn that the great majority of MT systems have eschewed explicit semantics. Only at the end of his article does Seligman allude to a form of semantics that could potentially refute Searle’s argument: a perceptually grounded semantics in which the classes and categories employed by an MT system would be learned through artificial perception of the real world. This is indeed an intriguing possibility, and given AI’s remarkable progress in recent years, it doesn’t appear entirely outlandish or far-fetched. My problem with Seligman’s position lies not so much in the feasibility of such an autonomous machine-learned semantics; rather, it has to do with its necessity. Simply put, neural machine translation (NMT) systems have become so good of late that one can’t help wonder how much of a difference a perceptually grounded explicit semantics could possibly make to these systems’ output quality. As it turns out, there are several articles dealing with MT and MAT in this collection which, one might argue, appear to have been overtaken by the stunning progress made of late by neural MT. The reordering techniques described by Masaaki Nagata in chapter 9 for MT between Japanese and English apply to the syntactic intermediate structures produced by statistical MT systems. As he himself recognizes at the end of his article, neural MT systems make no use of this kind of intermediate structure and have largely resolved the reordering problem that formerly plagued MT between these two very different languages. Even the pertinence of the EXPERT Project (described in Chapter 11), which set out to develop new hybrid data-driven approaches to translation, may need to be re-evaluated in the light of the quality of the output produced by today’s NMT systems. Much of the work in this EC-funded FP7 project focussed on improving what is presented as being the central component of the modern translator’s arsenal, that is, translation memories. And the project did propose sensible ways of enhancing the retrieval algorithms at the heart …

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