Search Results for author: Francis Ferraro

Found 26 papers, 5 papers with code

Transferring Semantic Knowledge Into Language Encoders

no code implementations14 Oct 2021 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.

Reading Comprehension Sentence Embedding

Neural Variational Learning for Grounded Language Acquisition

no code implementations20 Jul 2021 Nisha Pillai, Cynthia Matuszek, Francis Ferraro

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms.

Language Acquisition

Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

no code implementations30 Jun 2021 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.

Translation

Sampling Approach Matters: Active Learning for Robotic Language Acquisition

no code implementations16 Nov 2020 Nisha Pillai, Edward Raff, Francis Ferraro, Cynthia Matuszek

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora.

Active Learning Feature Selection +1

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

1 code implementation NeurIPS 2020 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 rely on stochastic backpropagation to handle the discrete variables.

Variational Inference

On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling

1 code implementation12 Oct 2020 Rajat Patel, Francis Ferraro

We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling.

Knowledge Graph Embedding Knowledge Graph Embeddings +2

Event Representation with Sequential, Semi-Supervised Discrete Variables

no code implementations NAACL 2021 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 into account.

Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning

no code implementations29 Jul 2020 Patrick Jenkins, Rishabh Sachdeva, Gaoussou Youssouf Kebe, Padraig Higgins, Kasra Darvish, Edward Raff, Don Engel, John Winder, Francis Ferraro, Cynthia Matuszek

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI.

Grounded language learning

Locality Preserving Loss: Neighbors that Live together, Align together

no code implementations7 Apr 2020 Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations.

Natural Language Inference Sentence Embeddings +1

SURFACE: Semantically Rich Fact Validation with Explanations

no code implementations31 Oct 2018 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.

General Classification

UMBC at SemEval-2018 Task 8: Understanding Text about Malware

no code implementations SEMEVAL 2018 Ankur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, SHimei Pan, Youngja Park, Anupam Joshi, Tim Finin

We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).

Semantic Proto-Roles

no code implementations TACL 2015 Drew Reisinger, Rachel Rudinger, Francis Ferraro, Craig Harman, Kyle Rawlins, Benjamin Van Durme

We present the first large-scale, corpus based verification of Dowty{'}s seminal theory of proto-roles.

Semantic Role Labeling

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