Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics

TL;DR

This paper integrates Dynamic Syntax with vector space semantics to model incremental language understanding, enabling real-time semantic plausibility assessment during dialogue processing.

Contribution

It introduces a novel framework combining Dynamic Syntax with tensor-based distributional semantics for incremental interpretation.

Findings

Successfully assigns semantic plausibility incrementally during parsing.

Provides a formal link between DS parsing and tensor contraction operations.

Demonstrates the framework with plausibility tensors in a working example.

Abstract

One of the fundamental requirements for models of semantic processing in dialogue is incrementality: a model must reflect how people interpret and generate language at least on a word-by-word basis, and handle phenomena such as fragments, incomplete and jointly-produced utterances. We show that the incremental word-by-word parsing process of Dynamic Syntax (DS) can be assigned a compositional distributional semantics, with the composition operator of DS corresponding to the general operation of tensor contraction from multilinear algebra. We provide abstract semantic decorations for the nodes of DS trees, in terms of vectors, tensors, and sums thereof; using the latter to model the underspecified elements crucial to assigning partial representations during incremental processing. As a working example, we give an instantiation of this theory using plausibility tensors of compositional distributional semantics, and show how our framework can incrementally assign a semantic plausibility measure as it parses phrases and sentences.