Search Results for author:
Found 26 papers, 8 papers with code
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
The Flickr30k dataset has become a standard benchmark for sentence-based image description.
Ranked #17 on
Image Retrieval
on Flickr30K 1K test
Evaluating Induced CCG Parsers on Grounded Semantic Parsing
We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis.
Phrase Grounding by Soft-Label Chain Conditional Random Field
In this paper, we formulate phrase grounding as a sequence labeling task where we treat candidate regions as potential labels, and use neural chain Conditional Random Fields (CRFs) to model dependencies among regions for adjacent mentions.
Ranked #8 on
Phrase Grounding
on Flickr30k Entities Test
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Multimedia Generative Script Learning for Task Planning
Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities.
Analyzing the Performance of Large Language Models on Code Summarization
We show that for the task of code summarization, the performance of these models on individual examples often depends on the amount of (subword) token overlap between the code and the corresponding reference natural language descriptions in the dataset.
Natural Language Inference from Multiple Premises
We define a novel textual entailment task that requires inference over multiple premise sentences.
Learning to Predict Denotational Probabilities For Modeling Entailment
We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities.
Towards Problem Solving Agents that Communicate and Learn
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
We propose to use the visual denotations of linguistic expressions (i. e. the set of images they describe) to define novel denotational similarity metrics, which we show to be at least as beneficial as distributional similarities for two tasks that require semantic inference.
An HDP Model for Inducing Combinatory Categorial Grammars
We introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text.
Collaborative Dialogue in Minecraft
We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios.
A Multi-Perspective Architecture for Semantic Code Search
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.
Learning to execute instructions in a Minecraft dialogue
The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment.
University of Illinois Submission to the SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
The objective of this shared task is to produce an inflected form of a word, given its lemma and a set of tags describing the attributes of the desired form.
Human-guided Collaborative Problem Solving: A Natural Language based Framework
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication.
A Framework for Bidirectional Decoding: Case Study in Morphological Inflection
Transformer-based encoder-decoder models that generate outputs in a left-to-right fashion have become standard for sequence-to-sequence tasks.
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned information from a trained model.
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
Machine unlearning (MUL) is an arising field in machine learning that seeks to erase the learned information of specific training data points from a trained model.
