Accepted Papers: IWCS 2015

We have 2 recently accepted papers to the IWCS conference which will take place in London, UK.

Title: Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches
Authors: Julian Hough, Casey Kennington, David Schlangen and Jonathan Ginzburg
Truly interactive dialogue systems need to construct meaning on at least a word-by-word basis. We propose desiderata for incremental semantics for dialogue models and systems, a task not heretofore attempted thoroughly. After laying out the desirable properties we illustrate how they are met by current approaches, comparing two incremental semantic processing frameworks: Dynamic Syntax enriched with Type Theory with Records (DS-TTR) and Robust Minimal Recursion Semantics with incremental processing (RMRS-IP). We conclude these approaches are not significantly different with regards to their semantic representation construction, however their purported role within semantic models and dialogue models is where they diverge.


Title: A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution
Authors: Casey Kennington, Livia Dia, David Schlangen
A large part of human communication involves referring to entities in the world, and often these entities are objects that are visually present for the interlocutors. A computer system that aims to resolve such references needs to tackle a complex task: objects and their visual features need to be determined, the referring expressions must be recognised, extra-linguistic information such as eye gaze or pointing gestures need to be incorporated — and the intended connection between words and world must be reconstructed. In this paper, we introduce a discriminative model of reference resolution that processes incrementally (i.e., word for word), is perceptually-grounded in the world, and improves when interpolated with information from gaze and pointing gestures. We evaluated our model and found that it performed robustly in a realistic reference resolution task, when compared to a generative model.