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Graph-based Neural Models for Dialogue Management


Description:

The GraphDial project sets out to advance the state-of-the-art in dialogue management. Current approaches to dialogue management often make use of neural models trained on dialogue data using supervised or reinforcement learning. These approaches have led to enhanced performance across a broad range of tasks, but also have two important shortcomings: (1) They rely on quite restrictive representations of the dialogue state (often based on fixed numbers of slots to fill); (2) They are dependent on large amounts of training data to learn the model parameters. The project will investigate an alternative approach to dialogue management based on the use of probabilistic graphs as core representation for the dialogue state. Graphs are well suited to capture rich interaction contexts including multiple entities and relations. They also facilitate the use of relational abstractions covering large portions of the state space in a compact and human-readable manner. Another central topic for the project is the use of weak supervision to train neural models for dialogue state tracking and action selection. Weak supervision allows machine learning models to be trained with indirect data extracted from noisy labelling functions or domain knowledge. The use of weak supervision is particularly attractive for dialogue management due to the difficulty of obtaining annotated data in most dialogue domains. The project intends to integrate a range of weak supervision signals, including user responses to grounding or clarification acts, heuristic rules and global constraints on the graph structure. To achieve these objectives, GraphDial will feature collaborations with leading researchers in the field of spoken dialogue systems, statistical relational learning, graph neural networks and human-robot interaction. The project will also be in contact with two Norwegian companies involved in the development of conversational technology to facilitate the dissemination of project results.


Project leader: Pierre Lison

Started: 2020

Ends: 2024

Category: Teknisk-industrielle institutter

Sector: Instituttsektor

Budget: 7998000

Institution: NORSK REGNESENTRAL

Address: Oslo