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Found 39 papers, 1 papers with code
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling
Event schemas can guide our understanding and ability to make predictions with respect to what might happen next.
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.
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.
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.
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.
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations.
GAIA: A Fine-grained Multimedia Knowledge Extraction System
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.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
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.
Cross-lingual Structure Transfer for Zero-resource Event Extraction
Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging.
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.
Cross-lingual Structure Transfer for Relation and Event Extraction
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.
A Research Platform for Multi-Robot Dialogue with Humans
This paper presents a research platform that supports spoken dialogue interaction with multiple robots.
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.
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings
In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family.
Multilingual Entity, Relation, Event and Human Value Extraction
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.
Incorporating Background Knowledge into Video Description Generation
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.
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.
STYLUS: A Resource for Systematically Derived Language Usage
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.
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.
The Case for Systematically Derived Spatial Language Usage
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.
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.
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.
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness.
Scalable Topical Phrase Mining from Text Corpora
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.
Finding Romanized Arabic Dialect in Code-Mixed Tweets
Recent computational work on Arabic dialect identification has focused primarily on building and annotating corpora written in Arabic script.
Assessing Divergence Measures for Automated Document Routing in an Adaptive MT System
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.
