This paper presents a novel approach to learning to solve simple arithmetic word problems.
Our system, ARIS, analyzes each of the sentences in the problem statement to identify the …

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 …

The ability to understand natural-language instructions is critical to building intelligent
agents that interact with humans. We present a system that learns to transform natural …

This paper proposes a novel approach for relation extraction from free text which is trained
to jointly use information from the text and from existing knowledge. Our model is based on …

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 …

In this paper we present Uniform Meaning Representation (UMR), a meaning representation
designed to annotate the semantic content of a text. UMR is primarily based on Abstract …

This paper introduces GEOS, the first automated system to solve unaltered SAT geometry
questions by combining text understanding and diagram interpretation. We model the …

We present a novel commentator system that learns language from sportscasts of simulated
soccer games. The system learns to parse and generate commentaries without any …

Abstract This paper introduces Logical Semantics with Perception (LSP), a model for
grounded language acquisition that learns to map natural language statements to their …

Recent investigations into grounded models of language have shown that holistic views of
language and perception can provide higher performance than independent views. In this …