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 • 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 • EMNLP 2020 • Manling Li, Qi Zeng, Ying Lin, Kyunghyun Cho, Heng Ji, Jonathan May, Nathanael Chambers, Clare Voss
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
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 • 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.
1 code implementation • EMNLP 2021 • Manling Li, Sha Li, Zhenhailong Wang, Lifu Huang, Kyunghyun Cho, Heng Ji, Jiawei Han, Clare Voss
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
no code implementations • ACL 2020 • Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
We present the first comprehensive, open source multimedia knowledge extraction system that takes a massive stream of unstructured, heterogeneous multimedia data from various sources and languages as input, and creates a coherent, structured knowledge base, indexing entities, relations, and events, following a rich, fine-grained ontology.
no code implementations • NAACL 2021 • Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi R. Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed Elsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions.
no code implementations • LREC 2020 • Di Lu, Ananya Subburathinam, Heng Ji, Jonathan May, Shih-Fu Chang, Avi Sil, Clare Voss
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
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 • IJCNLP 2019 • Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss
The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages.
no code implementations • NAACL 2019 • Matthew Marge, Stephen Nogar, Cory J. Hayes, Stephanie M. Lukin, Jesse Bloecker, Eric Holder, Clare Voss
This paper presents a research platform that supports spoken dialogue interaction with multiple robots.
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 • WS 2019 • Ahmed El-Kishky, Xingyu Fu, Aseel Addawood, Nahil Sobh, Clare Voss, Jiawei Han
In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family.
no code implementations • NAACL 2019 • Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.
no code implementations • EMNLP 2018 • Spencer Whitehead, Heng Ji, Mohit Bansal, Shih-Fu Chang, Clare Voss
We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents.
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 • Bonnie Dorr, Clare Voss
We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes.
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 • WS 2018 • Bonnie Dorr, Clare Voss
This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources.
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 • 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 • 13 Jun 2016 • Douglas Summers-Stay, Clare Voss, Taylor Cassidy
The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness.
no code implementations • NAACL 2016 • Di Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-Fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, Lance Kaplan
no code implementations • 24 Jun 2014 • Ahmed El-Kishky, Yanglei Song, Chi Wang, Clare Voss, Jiawei Han
Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition.
no code implementations • LREC 2014 • Clare Voss, Stephen Tratz, Jamal Laoudi, Douglas Briesch
Recent computational work on Arabic dialect identification has focused primarily on building and annotating corpora written in Arabic script.
no code implementations • LREC 2012 • Claire Jaja, Douglas Briesch, Jamal Laoudi, Clare Voss
This paper investigates the use of the Jensen-Shannon divergence measure for automatically routing new documents within a translation system with multiple MT engines to the appropriate custom MT engine in order to obtain the best translation.