Translating Negation: A Manual Error Analysis

By Federico Fancellu, Bonnie Lynn Webber. 2015

22 citations recorded by the source corpus.

Statistical Machine Translation has come a long way improving the translation quality of a range of different linguistic phenomena. With negation however, techniques proposed 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 current 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.

Subfields: Artificial Intelligence

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Citations in the corpus

How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs (2016, background): “…verb-particle constructions (Nießen and Ney, 2000; Loáiciga and Gulordava, 2016), generalization to unseen words (specifically, transliteration of names (Durrani et al., 2014)), and ensuring that polarity is maintained (Wetzel and Bond, 2012; Chen and Zhu, 2014; Fancellu and Webber, 2015).”
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation (2020, background): “Nevertheless, the last comprehensive study of the effect of negation in MT pertains to older, phrase-based models (Fancellu and Webber, 2015).”
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation (2022, background): “, 2019), machine translation (Fancellu and Webber, 2015; Hossain et al., 2020a), and QA (Ribeiro et al.”
On Integrating Discourse in Machine Translation (2017, background): “Subsequently, Fancellu and Webber (2015b) show that the translation model scoring is the cause of the errors in translating negation.”