--- title: "SMT Errors Requiring Grammatical Knowledge for — Alam 2015" description: "The paper presents a qualitative analysis of errors in English-to-Japanese statistical machine translation (SMT)." authors: ["Yukiko Sasaki Alam"] year: 2015 venue: "Proceedings of the 12th International Conference on Natural Language Processing" source: "https://aclanthology.org/W15-5923/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/papers/alam-2015-smt-errors-requiring-grammatical-knowledge/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/alam-2015-smt-errors-requiring-grammatical-knowledge/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2015](https://md.research.elamur.ai/free/papers/2015/page/2/) / SMT Errors Requiring Grammatical Knowledge for Prevention # SMT Errors Requiring Grammatical Knowledge for Prevention **Authors:** Yukiko Sasaki Alam · **Year:** 2015 · **Venue:** Proceedings of the 12th International Conference on Natural Language Processing · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/W15-5923/) · [PDF](https://aclanthology.org/W15-5923.pdf) · [OpenAlex](https://openalex.org/W2787604518) **Citations:** 1 citations (openalex-snapshot · 2026-10-14) ## Summary The paper presents a qualitative analysis of errors in English-to-Japanese statistical machine translation (SMT). The author manually tested the online system Google Language Tools on individual sentences. She identified three classes of errors that, in her view, cannot be avoided without grammatical knowledge. The first class concerns words that have a negative meaning but no negative form (few, little, seldom, hardly, etc.). In Japanese, such sentences require explicit negation in the predicate. The system, however, often translates these words as "a few" or "almost", which reverses the meaning. The second class arises from inserted adverbials that break up fixed constructions, so the system no longer recognizes them. The third class is related to ellipsis. Constituents omitted in the English sentence must be restored when Japanese omission rules or a different Head-Complement order require it. The paper contains no quantitative evaluation. The author concludes that such cases require syntactic analysis and grammatical rules, and that statistics alone are not enough. The paper shows the weak points of SMT for language pairs with very different grammars. ## Key points - The object of study is English-Japanese translation in the Google Language Tools service. The method is manual analysis of examples, without a corpus or metrics. - Words with implicit negation: the determiners few and little, and the adverbs little, seldom, rarely, scarcely, hardly and barely. The part of speech must be determined, since few can be a determiner, a pronoun or an adjective. The system translated sentences with little attention and was hardly surprising with the opposite meaning. By contrast, it translated could/can hardly constructions almost correctly. - Adverbials such as so far or long inside template phrases like "unable to …" or "have been geared" spoil the translation. When the insertion is removed, the result becomes correct. The author mentions the controlled skip parser (Yamada, 1996), but cautions that negative adverbs must not be skipped. - Ellipsis: the omitted "those who" must be restored in the Japanese translation. The elliptical comparisons as much and half as long also proved problematic. - Conclusion: adding new templates for each case is impractical. Syntactic parsing and rules for recognition and generation are needed. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) Topics: [Machine translation](https://md.research.elamur.ai/free/topics/machine-translation/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{alam2015smt, title = {{SMT Errors Requiring Grammatical Knowledge for Prevention}}, author = {Alam, Yukiko Sasaki}, booktitle = {Proceedings of the 12th International Conference on Natural Language Processing}, year = {2015}, url = {https://aclanthology.org/W15-5923/} } ``` - [← previous Simplifying Lexical Simplification: Do We Need Simplified Corpora?](https://md.research.elamur.ai/free/papers/glavas-2015-simplifying-lexical-simplification-do-we/) - [next → Solving General Arithmetic Word Problems](https://md.research.elamur.ai/free/papers/roy-2015-solving-general-arithmetic-word-problems/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/papers/2015/page/2/) ## References 11 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Bunt, H. and A Van Horck. 1996. Discontinuous Constituency. Mouton De Gruyter. 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