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Second language learners studying languages with a diverse set of prepositions often find preposition usage difficult to master, which can manifest in second language writing as preposition errors that appear to result from transfer from a native language, or interlingual errors. We envision a digital writing assistant for language learners and teachers that can provide targeted feedback on these errors. To address these errors, we turn to the task of preposition error detection, which remains an open problem despite the many methods that have been proposed. In this paper, we explore various classifiers, with and without neural network-based features, and finetuned BERT models for detecting preposition errors between verbs and their noun arguments.
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Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities
The wide accessibility of social media has provided linguistically under-represented communities with an extraordinary opportunity to create content in their native languages. This, however, comes with certain challenges in script normalization, particularly where the speakers of a language in a bilingual community rely on another script or orthography to write their native language. This paper addresses the problem of script normalization for several such languages that are mainly written in a Perso-Arabic script. Using synthetic data with various levels of noise and a transformer-based model, we demonstrate that the problem can be effectively remediated. We conduct a small-scale evaluation of real data as well. Our experiments indicate that script normalization is also beneficial to improve the performance of downstream tasks such as machine translation and language identification.
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- Award ID(s):
- 2109578
- PAR ID:
- 10451365
- Date Published:
- Journal Name:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
- Volume:
- 1
- Page Range / eLocation ID:
- 14466 to 14487
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2023.acl-long.809
