We demonstrate that an unlexicalized PCFG can parse much more accurately than previously
shown, by making use of simple, linguistically motivated state splits, which break down …

Visual question answering is fundamentally compositional in nature---a question like" where
is the dog?" shares substructure with questions like" what color is the dog?" and" where is …

We present a new part-of-speech tagger that demonstrates the following ideas:(i) explicit
use of both preceding and following tag contexts via a dependency network representation,(ii) …

We present an automatic approach to tree annotation in which basic nonterminal symbols are
alternately split and merged to maximize the likelihood of a training treebank. Starting with …

We present a novel generative model for natural language tree structures in which semantic
(lexical dependency) and syntactic (PCFG) structures are scored with separate models. This …

Suppose we want to build a system that answers a natural language question by representing
its semantics as a logical forxm and computing the answer given a structured database of …

A central problem in grounded language acquisition is learning the correspondences between
a rich world state and a stream of text which references that world state. To deal with the …

We present several improvements to unlexicalized parsing with hierarchically state-split
PCFGs. First, we present a novel coarse-to-fine method in which a grammar’s own hierarchical …

We present a generative model for the unsupervised learning of dependency structures. We
also describe the multiplicative combination of this dependency model with a model of …

GPT-3 can perform numerous tasks when provided a natural language prompt that contains
a few training examples. We show that this type of few-shot learning can be unstable: the …