Papers by Matthias Scheutz

6 papers
Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model (C18-1)

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Challenge: Existing word learning models are insensitive to input order effects, but they are noisy and only provide ambiguous information.
Approach: They propose a Bayesian cross-situational word learning model with an incremental memory-limited algorithm for predicting input order effects.
Outcome: The proposed model performs well on corpus data while being insensitive to input order effects.
Automating Dataset Production Using Generative Text and Image Models (2024.lrec-main)

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Challenge: a lack of benchmarks or data for natural language processing is hindering empirical methods . a new pipeline is proposed to reduce the burden of producing image and text datasets .
Approach: They propose a pipeline to reduce the research burden of producing image and text datasets when datasets may not exist.
Outcome: The proposed pipeline reduces the research burden of producing image and text datasets when datasets may not exist.
Developing a Corpus of Indirect Speech Act Schemas (2020.lrec-1)

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Challenge: Indirect speech acts (ISAs) involve utterances whose literal meanings are not identical to their intended meanings.
Approach: They propose a formal representation of ISA Schemas required for such testing, including a measure of the difficulty of a particular schema.
Outcome: The proposed model minimizes the amount of expert authoring needed and maximizes realism.
Reasoning Requirements for Indirect Speech Act Interpretation (2020.coling-main)

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Challenge: Existing systems that pretrain word and sentence embeddings to account for nearby linguistic context are unclear how to integrate extra-linguistic context into NLU.
Approach: They perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts.
Outcome: The proposed model is based on the domain-general patterns of reasoning involved and implements Answer Set programming.
Social Norms Guide Reference Resolution (2022.naacl-main)

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Challenge: Existing tools for natural language resolution fail to handle ambiguous referents . ambiguity arises when the language is underspecified or there are multiple candidate referent.
Approach: They investigate how pragmatic modulators outside of the linguistic content are critical for correct interpretation of referents in underspecified contexts.
Outcome: The proposed method can be used to resolve referents in human environments.
Towards a Conversation-Analytic Taxonomy of Speech Overlap (L18-1)

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Challenge: a taxonomy for classifying speech overlap in natural language dialogue is presented . the scheme classifies overlap on the basis of several features, including onset point, local dialogue history, and management behavior.
Approach: They propose a taxonomy for classifying speech overlap in natural language dialogue . they describe the various dimensions of the scheme and show how it was applied to a corpus of collaborative dialogue based on onset point, dialogue history, and management behavior .
Outcome: The proposed taxonomy classifies overlap on the basis of onset point, dialogue history, management behavior.

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