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        <title>Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification</title>
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    <abstract>It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Padó and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via cross-lingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g. hot ... while ... cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4–10% F1-score in Vietnamese and Hindi.</abstract>
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%0 Conference Proceedings
%T Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification
%A Roth, Michael
%A Upadhyay, Shyam
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F roth-upadhyay-2019-combining
%X It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Padó and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via cross-lingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g. hot ... while ... cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4–10% F1-score in Vietnamese and Hindi.
%R 10.18653/v1/N19-1390
%U https://aclanthology.org/N19-1390
%U https://doi.org/10.18653/v1/N19-1390
%P 3899-3905

Markdown (Informal)

[Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification](https://aclanthology.org/N19-1390) (Roth & Upadhyay, NAACL 2019)

• Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification (Roth & Upadhyay, NAACL 2019)

ACL

• Michael Roth and Shyam Upadhyay. 2019. Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3899–3905, Minneapolis, Minnesota. Association for Computational Linguistics.