Papers with CxG

4 papers
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.

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