When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection. Chaudhary, A., Yin, K., Anastasopoulos, A., & Neubig, G. In Moens, M., Huang, X., Specia, L., & Yih, S. W., editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6911–6929, Online and Punta Cana, Dominican Republic, November, 2021. Association for Computational Linguistics. ![When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun “wall” has different lexical manifestations in Spanish – “pared” refers to an indoor wall while “muro” refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting rules explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted rules in a language learning setup for two languages, Spanish and Greek, where we use the rules to teach non-native speakers when to translate a given ambiguous word into its different possible translations.
@inproceedings{chaudhary_when_2021,
address = {Online and Punta Cana, Dominican Republic},
title = {When is {Wall} a {Pared} and when a {Muro}?: {Extracting} {Rules} {Governing} {Lexical} {Selection}},
shorttitle = {When is {Wall} a {Pared} and when a {Muro}?},
url = {https://aclanthology.org/2021.emnlp-main.553},
doi = {10.18653/v1/2021.emnlp-main.553},
abstract = {Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun “wall” has different lexical manifestations in Spanish – “pared” refers to an indoor wall while “muro” refers to an outside wall. However, this variety of lexical distinction may not be obvious to non-native learners unless the distinction is explained in such a way. In this work, we present a method for automatically identifying fine-grained lexical distinctions, and extracting rules explaining these distinctions in a human- and machine-readable format. We confirm the quality of these extracted rules in a language learning setup for two languages, Spanish and Greek, where we use the rules to teach non-native speakers when to translate a given ambiguous word into its different possible translations.},
urldate = {2024-09-17},
booktitle = {Proceedings of the 2021 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
publisher = {Association for Computational Linguistics},
author = {Chaudhary, Aditi and Yin, Kayo and Anastasopoulos, Antonios and Neubig, Graham},
editor = {Moens, Marie-Francine and Huang, Xuanjing and Specia, Lucia and Yih, Scott Wen-tau},
month = nov,
year = {2021},
keywords = {explanation, lexical-selection},
pages = {6911--6929},
}
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