Search Results for author:
Found 36 papers, 5 papers with code
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains
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.
What Can a Generative Language Model Answer About a Passage?
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.
InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research
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.
The Search for Agreement on Logical Fallacy Annotation of an Infodemic
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.
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response
We introduce a pipeline to create Field Ready Instruction Decoding Agent (FRIDA) models, where domain experts and linguists combine their knowledge to make high-quality seed data that is used to generate synthetic data for fine-tuning.
Assessing Language Comprehension in Large Language Models Using Construction Grammar
CxG is well-suited for this purpose because provides a theoretical basis to construct targeted evaluation sets.
Human-Robot Dialogue Annotation for Multi-Modal Common Ground
In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners.
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration.
Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis and Corpus Release
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.
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
In this work, we study how users interact with QA systems in the absence of sufficient information to assess their predictions.
Dialogue-AMR: Abstract Meaning Representation for Dialogue
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.
Abstract Meaning Representation
Natural Language Understanding
Augmenting Abstract Meaning Representation for Human-Robot Dialogue
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.
Visual Understanding and Narration: A Deeper Understanding and Explanation of Visual Scenes
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?'
Towards a Computational Lexicon for Moroccan Darija: Words, Idioms, and Constructions
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.
Constructing an Annotated Corpus of Verbal MWEs for English
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.
Can You Spot the Semantic Predicate in this Video?
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.
Automatically Extracting Qualia Relations for the Rich Event Ontology
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.
Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue.
Object and Text-guided Semantics for CNN-based Activity Recognition
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.
Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue
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.
The Rich Event Ontology
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.
Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task
Robot-directed communication is variable, and may change based on human perception of robot capabilities.
Applying the Wizard-of-Oz Technique to Multimodal Human-Robot Dialogue
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.
Comprehensive and Consistent PropBank Light Verb Annotation
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).
PropBank: Semantics of New Predicate Types
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.
