Search Results for author:
Found 34 papers, 7 papers with code
POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events
Knowledge about outcomes is critical for complex event understanding but is hard to acquire.
A General Framework for Auditing Differentially Private Machine Learning
We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice.
PASTA: A Dataset for Modeling Participant States in Narratives
Often, these participant states are not explicitly mentioned in the narrative, left to be filled in via common-sense or inference.
Neural Bregman Divergences for Distance Learning
Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e. g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space.
RevUp: Revise and Update Information Bottleneck for Event Representation
We reparameterize the model's discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure.
Continuously Generalized Ordinal Regression for Linear and Deep Models
Our method, which we term continuously generalized ordinal logistic, significantly outperforms the standard ordinal logistic model over a thorough set of ordinal regression benchmark datasets.
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
Learning to understand grounded language, which connects natural language to percepts, is a critical research area.
Transferring Semantic Knowledge Into Language Encoders
We introduce semantic form mid-tuning, an approach for transferring semantic knowledge from semantic meaning representations into transformer-based language encoders.
Discriminative and Generative Transformer-based Models For Situation Entity Classification
We re-examine the situation entity (SE) classification task with varying amounts of available training data.
Neural Variational Learning for Grounded Language Acquisition
We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms.
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment
We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective.
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora.
A Discrete Variational Recurrent Topic Model without the Reparametrization Trick
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.
On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling
We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling.
Event Representation with Sequential, Semi-Supervised Discrete Variables
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
Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI.
Locality Preserving Loss: Neighbors that Live together, Align together
We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations.
The Universal Decompositional Semantics Dataset and Decomp Toolkit
We present the Universal Decompositional Semantics (UDS) dataset (v1. 0), which is bundled with the Decomp toolkit (v0. 1).
?`Es un pl\'atano? Exploring the Application of a Physically Grounded Language Acquisition System to Spanish
In this paper we describe a multilingual grounded language learning system adapted from an English-only system.
Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization
We present a family of novel methods for embedding knowledge graphs into real-valued tensors.
Team UMBC-FEVER : Claim verification using Semantic Lexical Resources
We describe our system used in the 2018 FEVER shared task.
SURFACE: Semantically Rich Fact Validation with Explanations
Judging the veracity of a sentence making one or more claims is an important and challenging problem with many dimensions.
Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems
KGCleaner is a framework to identify and correct errors in data produced and delivered by an information extraction system.
UMBC at SemEval-2018 Task 8: Understanding Text about Malware
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
We study how different frame annotations complement one another when learning continuous lexical semantics.
Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling.
A Survey of Current Datasets for Vision and Language Research
Integrating vision and language has long been a dream in work on artificial intelligence (AI).
Semantic Proto-Roles
We present the first large-scale, corpus based verification of Dowty{'}s seminal theory of proto-roles.
