Papers by Claire Bonial
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. |
A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have been developed without a theoretical framework . evaluating and improving LLMs will benefit from theoretical frameworks that enable comparison of structures of human language and model of language built up by LLM. |
| Approach: | They propose to use a construction grammar schema corpus to compare human grammar to LLMs' model of language. |
| Outcome: | The proposed corpus shows that even the largest LLMs are limited to more substantive constructions and do not recognize similarity of purely schematic constructions. |
Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs (2025.acl-long)
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| Challenge: | a misalignment or misunderstanding can disrupt communication, leading to confusion or conflict. |
| Approach: | They study failures of grounding in Ubuntu IRC datasets to identify misalignments . they find disruptions in conversational flow are driven by a divergence in beliefs . |
| Outcome: | The findings show that misalignment in common ground can disrupt communication . the study also shows that miscommunications can lead to confusion or conflict . |
Automatically Extracting Qualia Relations for the Rich Event Ontology (C18-1)
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| Challenge: | a new study uses qualia relations extracted from the Suggested Upper Merged Ontology to extract information about entities . human annotators find qualia relationships and origins of the information to be reasonable . |
| Approach: | They propose to extract qualia from the Generative Lexicon to extract quealia . they assume the theoretical framework of the Generative Lexicons . |
| Outcome: | The proposed method extracts information from the Suggested Upper Merged Ontology (SUMO) human annotators find the extracted information to be reasonable, the authors show . |
The Search for Agreement on Logical Fallacy Annotation of an Infodemic (2022.lrec-1)
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Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
| Challenge: | a parallel "infodemic" has emerged with the COVID-19 pandemic . logical fallacies can be subtly encoded in the structure of a document across multiple sentences . |
| Approach: | They evaluate an annotation schema for labeling logical fallacy types using linguist annotations . they propose to use a machine learning algorithm to train annotators for fallacy detection . |
| Outcome: | The proposed annotation schema is clear and non-overlapping for manual and system assignment. |
Dialogue Structure Annotation for Multi-Floor Interaction (L18-1)
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David Traum, Cassidy Henry, Stephanie Lukin, Ron Artstein, Felix Gervits, Kimberly Pollard, Claire Bonial, Su Lei, Clare Voss, Matthew Marge, Cory Hayes, Susan Hill
| Challenge: | Existing annotation schemes do not address dialogue structure. |
| Approach: | They propose an annotation scheme for meso-level dialogue structure that clusters utterances from multiple participants and floors into units according to realization of an initiator's intent. |
| Outcome: | The proposed annotation scheme is used to annotate a corpus of human-robot interaction dialogues. |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
What Else Do I Need to Know? The Effect of Background Information on Users’ Reliance on QA Systems (2023.emnlp-main)
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Navita Goyal, Eleftheria Briakou, Amanda Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare Voss, Marine Carpuat, Hal Daumé III
| Challenge: | Existing NLP systems can only access the retrieved context to determine the answer, resulting in a knowledge gap between the information that is required to answer the question and the information available to assess the model’s correctness. |
| Approach: | They ask whether adding relevant background helps mitigate users’ over-reliance on predictions. |
| Outcome: | The proposed approach reduces over-reliance on model predictions even in the absence of sufficient information to assess their correctness. |
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. |