SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task

Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief state input. The self-feeding belief state input increases the accuracy in multi-turn dialogue by summarizing previous dialogue. Also, we newly developed a slot-gate auxiliary task. This new auxiliary task helps classify whether a slot is mentioned in the dialogue. Our model achieved the best score in a few-shot setting for four domains on multiWOZ 2.0.Comment: Accepted in INTERSPEECH 202

Fully statistical neural belief tracking

This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models

Computationally viable handling of beliefs in arguments for persuasion

Computational models of argument are being developed to capture aspects of how persuasion is undertaken. Recent proposals suggest that in a persuasion dialogue between some agents, it is valuable for each agent to model how arguments are believed by the other agents. Beliefs in arguments can be captured by a joint belief distribution over the arguments and updated as the dialogue progresses. This information can be used by the agent to make more intelligent choices of move in the dialogue. Whilst these proposals indicate the value of modelling the beliefs of other agents, there is a question of the computational viability of using a belief distribution over all the arguments. We address this problem in this paper by presenting how probabilistic independence can be leveraged to split this joint distribution into an equivalent set of distributions of smaller size. Experiments show that updating the belief on the split distribution is more efficient than performing updates on the joint distribution

A modified approach of POMDP-based dialogue management

This thesis applies the theory of history information space for a thorough study of dialogue management in major approaches, ranging from the classical approach based upon finite state machine to the most recent approach using partially observable Markov decision process (PODMP). While most of the approaches use various techniques to estimate system state, the POMDP-based approach avoids state estimation and uses belief state for decision making. In addition, it provides a mechanism to model uncertainty and allows for errorrecovery. PODMP-based dialogue management demonstrates undeniable advantages in the handling of input uncertainty over all the other approaches. However, applying Markovian over the belief-state space in the current POMDP models causes significant loss of valuable information in dialogue history, leading to untruthful recognition of user\u27s intention. To improve the performance of POMDP-based dialogue management this thesis introduces belief history into the planning process, and uses not only the current but also the previous belief state for the determination of actions. In the new approach, all changes of belief state require a validation with domain constraints, and an invalid change results in a modification to the actions provided by the POMDP solver. Experiments show that this new approach is able to handle uncertainty caused by user\u27s lack of domain knowledge and practical constraints, thus becoming more accurate in intention recognition