Translating Negation: A Manual Error Analysis

Federico Fancellu, Bonnie Webber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Statistical Machine Translation has come a long way improving the translation quality of a range of different linguistic phenomena. With negation however, techniques pro- posed and implemented for improving translation performance on negation have simply followed from the developers’ beliefs about why performance is worse. These beliefs, however, have never been validated by an error analysis of the translation output. In contrast, the cur- rent paper shows that an informative empirical error analysis can be formulated in terms of (1) the set of semantic elements involved in the meaning of negation, and (2) a small set of string-based operations that can characterise errors in the translation of those elements. Results on a Chinese-to-English translation task confirm the robustness of our analysis cross-linguistically and the basic assumptions can inform an automated investigation into the causes of translation errors. Conclusions drawn from this analysis should guide future work on improving the translation of negative sentences.
Original languageEnglish
Title of host publicationProceedings of the Second Workshop on Extra-Propositional Aspects of Meaning in Computational Semantics (ExProM 2015)
Place of PublicationDenver, Colorado
PublisherAssociation for Computational Linguistics
Pages2-11
Number of pages10
Publication statusPublished - 1 Jun 2015

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