no code implementations • LREC 2022 • Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators.
no code implementations • IWCS (ACL) 2021 • Claire Bonial, Mitchell Abrams, David Traum, Clare Voss
We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors.
no code implementations • DMR (COLING) 2020 • Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, Clare Voss
This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19.
no code implementations • EMNLP (MRQA) 2021 • Douglas Summers-Stay, Claire Bonial, Clare Voss
Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair.
no code implementations • 26 Oct 2023 • Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Taylor Hudson, Ron Arstein, Clare Voss, David Traum
Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans.
no code implementations • 23 May 2023 • Navita Goyal, Eleftheria Briakou, Amanda Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare R. Voss, Marine Carpuat, Hal Daumé III
In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions.
no code implementations • LREC 2020 • Claire Bonial, Lucia Donatelli, Mitchell Abrams, Stephanie M. Lukin, Stephen Tratz, Matthew Marge, Ron artstein, David Traum, Clare Voss
This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems.
no code implementations • WS 2019 • Claire Bonial, Lucia Donatelli, Stephanie M. Lukin, Stephen Tratz, Ron artstein, David Traum, Clare Voss
We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance.
no code implementations • 31 May 2019 • Stephanie M. Lukin, Claire Bonial, Clare R. Voss
We describe the task of Visual Understanding and Narration, in which a robot (or agent) generates text for the images that it collects when navigating its environment, by answering open-ended questions, such as 'what happens, or might have happened, here?'
no code implementations • COLING 2018 • Christopher Reale, Claire Bonial, Heesung Kwon, Clare Voss
We propose a method to improve human activity recognition in video by leveraging semantic information about the target activities from an expert-defined linguistic resource, VerbNet.
no code implementations • COLING 2018 • Jamal Laoudi, Claire Bonial, Lucia Donatelli, Stephen Tratz, Clare Voss
In this paper, we explore the challenges of building a computational lexicon for Moroccan Darija (MD), an Arabic dialect spoken by over 32 million people worldwide but which only recently has begun appearing frequently in written form in social media.
no code implementations • COLING 2018 • Abigail Walsh, Claire Bonial, Kristina Geeraert, John P. McCrae, Nathan Schneider, Clarissa Somers
This paper describes the construction and annotation of a corpus of verbal MWEs for English, as part of the PARSEME Shared Task 1. 1 on automatic identification of verbal MWEs.
no code implementations • COLING 2018 • Ghazaleh Kazeminejad, Claire Bonial, Susan Windisch Brown, Martha Palmer
Commonsense, real-world knowledge about the events that entities or {``}things in the world{''} are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world.
no code implementations • WS 2018 • Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Matthew Marge, Cassidy Henry, Ron Arstein, David Traum, Clare R. Voss
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue.
no code implementations • 4 May 2018 • Sungmin Eum, Christopher Reale, Heesung Kwon, Claire Bonial, Clare Voss
We further improve upon the multitask learning approach by exploiting a text-guided semantic space to select the most relevant objects with respect to the target activities.
no code implementations • 17 Oct 2017 • Claire Bonial, Matthew Marge, Ron artstein, Ashley Foots, Felix Gervits, Cory J. Hayes, Cassidy Henry, Susan G. Hill, Anton Leuski, Stephanie M. Lukin, Pooja Moolchandani, Kimberly A. Pollard, David Traum, Clare R. Voss
We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data.
no code implementations • WS 2017 • Susan Brown, Claire Bonial, Leo Obrst, Martha Palmer
In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations.
no code implementations • WS 2017 • Matthew Marge, Claire Bonial, Ashley Foots, Cory Hayes, Cassidy Henry, Kimberly Pollard, Ron artstein, Clare Voss, David Traum
Robot-directed communication is variable, and may change based on human perception of robot capabilities.
no code implementations • 10 Mar 2017 • Matthew Marge, Claire Bonial, Brendan Byrne, Taylor Cassidy, A. William Evans, Susan G. Hill, Clare Voss
Our overall program objective is to provide more natural ways for soldiers to interact and communicate with robots, much like how soldiers communicate with other soldiers today.
no code implementations • LREC 2016 • Claire Bonial, Martha Palmer
Recent efforts have focused on expanding the annotation coverage of PropBank from verb relations to adjective and noun relations, as well as light verb constructions (e. g., make an offer, take a bath).
no code implementations • LREC 2014 • Claire Bonial, Julia Bonn, Kathryn Conger, Jena D. Hwang, Martha Palmer
This research focuses on expanding PropBank, a corpus annotated with predicate argument structures, with new predicate types; namely, noun, adjective and complex predicates, such as Light Verb Constructions.