Over the years, integer linear programs have been employed to model infe...
Can language models transform inputs to protect text classifiers against...
This paper addresses the question of how to efficiently learn from disjo...
Conversational search has seen increased recent attention in both the IR...
Models trained via empirical risk minimization (ERM) are known to rely o...
In question answering requiring common sense, language models (e.g., GPT...
We present a method for exploring regions around individual points in a
...
Symbolic knowledge can provide crucial inductive bias for training neura...
Given the prevalence of pre-trained contextualized representations in to...
In this work, we introduce X-FACT: the largest publicly available
multil...
Smart databases are adopting artificial intelligence (AI) technologies t...
Understanding how linguistic structures are encoded in contextualized
em...
Word vector embeddings have been shown to contain and amplify biases in ...
In this paper, we study the response of large models from the BERT famil...
Although current CCG supertaggers achieve high accuracy on the standard ...
While language embeddings have been shown to have stereotyping biases, h...
Discourse parsing is largely dominated by greedy parsers with
manually-d...
Language representations are known to carry stereotypical biases and, as...
Various natural language processing tasks are structured prediction prob...
In this paper, we observe that semi-structured tabulated text is ubiquit...
Recent neural network-driven semantic role labeling (SRL) systems have s...
One of the goals of natural language understanding is to develop models ...
While neural models show remarkable accuracy on individual predictions, ...
Word embeddings carry stereotypical connotations from the text they are
...
Automatically analyzing dialogue can help understand and guide behavior ...
Today, the dominant paradigm for training neural networks involves minim...
There is a mismatch between the standard theoretical analyses of statist...
Predicting structured outputs can be computationally onerous due to the
...
Semantic relations are often signaled with prepositional or possessive
m...
Many recent works have designed accelerators for Convolutional Neural
Ne...
This document describes an inventory of 50 semantic labels designed to
c...
We consider the semantics of prepositions, revisiting a broad-coverage
a...
We present the first corpus annotated with preposition supersenses,
unle...
IllinoisSL is a Java library for learning structured prediction models. ...