Raquel Fernández

Classifying ellipsis in dialogue: a machine learning approach

This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We extract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. Both learners perform well, yielding similar success rates of approx 90%. The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features.

Abstract models for dialogue protocols

We examine a variety of dialogue protocols, taking inspiration from two fields: natural language dialogue modelling and multiagent systems. In communicative interaction, one can identify different features that may increase the complexity of the dialogue structure. This motivates a hierarchy of abstract models for protocols that takes as a starting point protocols based on deterministic finite automata. From there, we proceed by looking at particular examples that justify either an enrichment or a restriction of the initial model.

Scaling up from Dialogue to Multilogue: Some Principles and Benchmarks

The paper considers how to scale up dialogue protocols to multilogue, settings with multiple conversationalists. We extract two benchmarks to evaluate scaled up protocols based on the long distance resolution possibilities of non-sentential utterances in dialogue and multilogue in the British National Corpus. In light of these benchmarks, we then consider three possible transformations to dialogue protocols, formulated within an issue-based approach to dialogue management. We show that one such transformation yields protocols for querying and assertion that fulfill these benchmarks.

A dynamic logic formalisation of the dialogue gameboard

This paper explores the possibility of using the paradigm of Dynamic Logic (DL) to formalise information states and update processes on information states. In particular, we present a formalisation of the dialogue gameboard introduced by Jonathan Ginzburg. From a more general point of view, we show that DL is particularly well suited to develop rigorous formal foundations for an approach to dialogue dynamics based on information state updates.