110 citations · 130 across the 9 of their papers we have counts for
15 papers
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Ishani Mondal, Michelle Yuan, Anandhavelu N +5
Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; howe…
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Sai Vallurupalli, Sayontan Ghosh, Katrin Erk +2
Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are abl…
PASTA: A Dataset for Modeling Participant States in Narratives
Sayontan Ghosh, Mahnaz Koupaee, Isabella Chen +3
The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead…
RevUp: Revise and Update Information Bottleneck for Event Representation
Mehdi Rezaee, Francis Ferraro
The existence of external (``side'') semantic knowledge has been shown to result in more expressive computational event models. To enable the use of side information that may be no…
Transferring Semantic Knowledge Into Language Encoders
Mohammad Umair, Francis Ferraro
We introduce semantic form mid-tuning, an approach for transferring semantic knowledge from semantic meaning representations into transformer-based language encoders. In mid-tuning…
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
Ashwinkumar Ganesan, Francis Ferraro, Tim Oates
We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by train…
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
Mehdi Rezaee, Francis Ferraro
We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not…
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
Rajat Patel, Francis Ferraro
We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling. We show that a language model-inspire…
Event Representation with Sequential, Semi-Supervised Discrete Variables
Mehdi Rezaee, Francis Ferraro
Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge…
The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha +9
We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantic…
SURFACE: Semantically Rich Fact Validation with Explanations
Ankur Padia, Francis Ferraro, Tim Finin
Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions. The recent FEVER task asked participants to classify inpu…
Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems
Ankur Padia, Francis Ferraro, Tim Finin
KGCleaner is a framework to identify and correct errors in data produced and delivered by an information extraction system. These tasks have been understudied and KGCleaner is the…
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
Francis Ferraro, Adam Poliak, Ryan Cotterell +1
We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that cons…
Visual Storytelling
Ting-Hao, Huang, Francis Ferraro +13
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND…
A Survey of Current Datasets for Vision and Language Research
Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao +5
Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vi…