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. 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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