Papers by Mitchell Abrams
Dialogue-AMR: Abstract Meaning Representation for Dialogue (2020.lrec-1)
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Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron Artstein, David Traum, Clare Voss
| Challenge: | Abstract Meaning Representation (AMR) does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context. |
| Approach: | They propose a schema that enriches Abstract Meaning Representation (AMR) it provides a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. |
| Outcome: | The proposed schema provides a semantic representation for facilitating Natural Language Understanding (NLU) in human-robot dialogue systems. |
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. |
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. |
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus (2024.lrec-main)
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Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor A. Hudson, Cory J. Hayes, Kimberly Pollard, Anthony Baker, Ashley N. Foots, Ron Artstein, Felix Gervits, Mitchell Abrams, Cassidy Henry, Lucia Donatelli, Anton Leuski, Susan G. Hill, David Traum, Clare Voss
| Challenge: | The corpus contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |
| Approach: | They present the Situated Corpus Of Understanding Transactions, a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. |
| Outcome: | The Situated Corpus Of Understanding Transactions (SCOUT) contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterrances per dialogue. |