Papers by Arabella Sinclair

7 papers
Construction Repetition Reduces Information Rate in Dialogue (2022.aacl-main)

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Challenge: We observe that construction usage lowers the information content of utterances.
Approach: They propose to use construction repetition to mitigate information rate in English open-domain spoken dialogues.
Outcome: The proposed method lowers the information content of utterances, while increasing the frequency and density of repetition.
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models (2022.starsem-1)

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Challenge: Existing approaches to NLP tasks rely on pre-trained language models, but some do not.
Approach: They propose a natural language inference task to test pre-trained language models for logical reasoning capabilities.
Outcome: The proposed language model performs better than other models across logical connectives and reasoning domains, but is sensitive to lexical and syntactic variations in the realisation of logical statements.
Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue (2026.acl-long)

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Challenge: a method to model utterance production is based on information-theoretic notions of cost . a technique to generate alternative sets of utterables is proposed .
Approach: They propose a procedure to generate both types of alternative sets using language models.
Outcome: The proposed procedure allows for speaker- and listener-oriented interpretations of different cost measures.
Do Language Models Exhibit Human-like Structural Priming Effects? (2024.findings-acl)

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Challenge: a recent exposure to a structure facilitates processing of the same structure, a study finds . structural priming is well attested in humans, for both language production and comprehension .
Approach: They use the structural priming paradigm to investigate where priming effects manifest . they find that rarer elements within a prime increase priming effect .
Outcome: The findings provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models.
Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts (2020.emnlp-main)

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Challenge: Subsequent references exploit the common ground accumulated by the interlocutors and tend to be shorter and reuse expressions that were effective in previous mentions.
Approach: They propose a model that generates first and subsequent references in visually grounded dialogue . they also implement a reference resolution system to assess the referring effectiveness .
Outcome: The proposed model produces better, more effective referring utterances than one not grounded in the dialogue context.
Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations (2022.tacl-1)

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Challenge: a rich literature has emerged in the last few years addressing these questions, including whether specific LMs have acquired specific linguistic constructions.
Approach: They introduce a novel metric and release Prime-LM, a large corpus where they control for various linguistic factors that interact with priming strength.
Outcome: The proposed model can learn abstract structural information independent of the structure of a sentence and is able to perform tasks that require natural language understanding skills.
Is Information Density Uniform in Task-Oriented Dialogues? (2021.emnlp-main)

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Challenge: Evidence for the uniform information density principle has been found at many levels of language production.
Approach: They propose to use the Uniform Information Density principle to test whether and within which contextual units it holds in task-oriented dialogues.
Outcome: The proposed method is able to reduce fluctuations in the density of the information transmitted.

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