Papers with Linguistic theories
Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study (2023.findings-emnlp)
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| Challenge: | Code-switching (CSW) text generation is a popular solution to address data scarcity. |
| Approach: | They compare linguistic theories, lexical replacements and back-translation approaches to Egyptian Arabic-English CSW. |
| Outcome: | The proposed methods perform best on machine translation and quality evaluation. |
Characterizing Idioms: Conventionality and Contingency (2022.acl-long)
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| Challenge: | idioms have non-canonical meanings, but non-conventional meanings are contingent on other words . a recent study shows that idiomatic expressions are not homogeneous among idiomas . |
| Approach: | They propose to use a contingency relationship between words in an idiom and non-canonical meanings of words in the idiome. |
| Outcome: | a new study shows that idioms fall at the expected intersection of the two dimensions, but that the dimensions themselves are not correlated. |
Which questions should I answer? Salience Prediction of Inquisitive Questions (2024.emnlp-main)
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| Challenge: | Recent work in NLP has taken advantage of question generation capabilities of LLMs to enhance a wide range of applications. |
| Approach: | They propose a salience predictor for inquisitive questions that is instruction-tuned . they show that highly salient questions are empirically more likely to be answered in the same article . |
| Outcome: | The proposed model is based on linguist-annotated salience scores of 1,766 questions . it shows that answering salient questions improves comprehension of the text . |
Causal Interventions Reveal Shared Structure Across English Filler–Gap Constructions (2025.emnlp-main)
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| Challenge: | Language Models (LMs) have emerged as powerful sources of evidence for linguists seeking to develop theories of syntax. |
| Approach: | They propose to use causal interpretability methods to characterize abstract mechanisms that LMs learn to use by transferring a wh-filler-gap structure into a gap-less c++ class. |
| Outcome: | The proposed methods can characterize the abstract mechanisms that LMs learn to use, and challenge claims that they can be learned only with strong innate priors. |