Representing Common Ground in a Multi-Source Graph Structure for Dialogue Analysis
Authors: Maria Di Maro, Francesco Cutugno (Università di Bari Aldo Moro, 2021)
Topic: Chatbots and Dialogue Systems

Abstract
Nowadays, the use of graph databases combined with textual corpora analysis seems to play a pivotal role in supporting dialogue systems design and implementation. However, dialogues are rarely put in an explicit relationship with the graph structures representing the knowledge domain. In this work, we show how native graph databases provide a framework for a deeper understanding about the use of domain items during dialogue. We describe a multiple-source data collection procedure and we describe how linguistic concepts related to common ground can be found in graph structures. We also describe different patterns that can be detected in the obtained graph structures and discuss their implications in the design of dialogue systems for the movie recommendation task.

Introduction
Graph-based data have become popular as support tools for a number of Natural Language Processing tasks, from Word Sense Disambiguation using node embeddings <cit.>, to Knowledge Base Collection <cit.> and Fraud Detection <cit.>. Most recent approaches involving the use of graph databases converge towards numerical representations of the included items for use by machine learning algorithms <cit.>. Knowledge structures using Labelled Property Graphs, however, are typically designed to be more interpretable by human researchers, who can also setup informative queries to extract latent knowledge from a number of cross-referenced resources. This suggests that the same resources supporting dialogue systems can be queried by human experts to extract deeper understanding about the use of domain items, supporting explainability. This implies that it is necessary to explicitly cross-reference dialogue corpora with domain knowledge, while the two are typically considered separately. In this paper, we describe a multiple-source data collection procedure to cross-reference an annotated English dialogue corpus covering the movie recommendation task, the Internet Movie Database and Wikidata. We present the relationship common ground concepts (see Section <ref>) have with graph structures and we present a set of examples concerning dialogue analysis performed in the graph. The main research questions are: * Q1: How to represent dialogues and domain knowledge in a single graph structure? * Q2: Is it possible to formalise common ground concepts guiding dialogue analysis in the resulting graph? * Q3: Is it possible to use the resulting graph to extract interpretable patterns to guide dialogue systems design? The paper is organised as follows: in Section <ref> we describe the considered resources and how they were cross-referenced in a graph database. In Section <ref>, we provide a deeper discussion about Common Ground representations and their relationship with graph databases. Section <ref> shows how dialogue history, connected with domain knowledge, can be queried to extract knowledge characterising the use of domain items in the considered (sub-)dialogues. This is of interest for the open challenge concerning the study of Argumentation-Based Dialogue which, as opposed to Argumentation-Based Inference, is an area that is still lacking a theoretical framework of reference <cit.>.

Conclusion
Graph databases have a great potential as linguistic tools for dialogue analysis, modelling interaction dynamics and domain knowledge in the same resource. While this representation provides a powerful support to dialogue systems through machine learning, it can be integrated by more informed design based on a qualitative analysis of the same graph structures. We presented a data collection procedure for a large graph database representing dialogue dynamics explicitly linked to domain knowledge. In the representation of such data, different kinds of Common Ground were identified as sub-graphs. We then, used it to investigate the choice of domain items supporting sociable conversational strategies. Concerning the research questions we were interested in investigating, the data collection procedure presented in Section <ref> presents how a graph database can organise and connect, at the same time, both dialogue history and domain knowledge, providing an example answering Q1 for the specific case of the movies recommendation dialogues. This is, then, generalised in Section <ref> by identifying linguistic concepts related to common ground that are described in terms of sub-graphs, independently of the specific domain. Recurring patterns involving both dialogue and common ground structures can, then, be identified using graph traversal queries. Specifically, we have characterised the different use of MOVIE and PERSON nodes depending on the order of appearance in a pattern where the referring nodes are linked by the relationship WORKED_IN, in the CCG. By analysing the sociable strategies involved in the patterns in which the specific domain items may occur, we described how the different use of the same node categories in opposite patterns (i.e., MOVIE followed by PERSON; PERSON followed by MOVIE) can be typical of a specific recommendation dialogue phase. The obtained data describe a way of managing Exploration and Exploitation phases informing the design of conversational recommender systems. We have also shown that, in selecting which PERSON items to use when adopting sociable strategies, RECOMMENDERs seem to prefer PERSONs with mid-skewed distributions over movie genres, suggesting that opinions about these PERSONs may give insight about sub-genres. Future work will consist of empowering the analyses by relating more complex numerical item representations, like node embeddings, to dialogue dynamics.