Papers with CxG
Stability of Syntactic Dialect Classification over Space and Time (2022.coling-1)
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| Challenge: | a paper examines the degree to which dialect classifiers remain stable over time . it finds that the models remain robust over time with a fixed decay rate . |
| Approach: | They construct a test set for 12 dialects of English that spans three years at monthly intervals with a fixed spatial distribution across 1,120 cities. |
| Outcome: | The proposed model can reveal linguistic variation over space and time. |
Enhancing Language Representation with Constructional Information for Natural Language Understanding (2023.acl-long)
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| Challenge: | Recent advances in natural language processing focus on acquiring lexico-semantic information. |
| Approach: | They propose a construction grammar which highlights the pairings of form and meaning to enrich language representation. |
| Outcome: | The proposed model is superior to existing models on a variety of NLU tasks. |
CxGGEC: Construction-Guided Grammatical Error Correction (2025.acl-long)
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| Challenge: | Current GEC methods rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. |
| Approach: | They propose to use construction grammar to capture underlying language patterns and guide corrections by decoding construction tokens into their original forms and correcting erroneous tokens. |
| Outcome: | The proposed model captures underlying language patterns and corrects erroneous construction tokens on English and Chinese benchmarks. |
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative (2022.emnlp-main)
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| Challenge: | Construction Grammar posits constructions as the central building blocks of language . human-like performance of pretrained language models on many NLP tasks has been alleged . |
| Approach: | They propose to use construction grammar to posit constructions as the central building blocks of language . they conduct experiments with three pretrained language models to examine their ability to classify and understand English comparative correlative . |
| Outcome: | The proposed models are able to recognise the English comparative correlative (CC) but fail to use its meaning. |