# Scalable Multi-Domain Dialogue State Tracking

### Overview

• The language understanding module outputs are used to delexicalize the user utterances, which are processed by the DST for feature extraction.
• We then integrate a separate candidate generation step that estimates a set of slot value candidates using the local conversation context, as well as possibly external knowledge sources.
• DST operates only on these candidates, resulting in an approach scalable to large and rich datasets.

### Architecture

• Model the dialogue state as a joint distribution across all slots and make a simplifying assumption of factoring the joint distribution as a product of a distribution for each slot.

Candidate Set

• Candidate set for a slot is defined as the set of values of that slot, along with the scores.
•  Let ${C_s}^t$ be the candidate set for a turn t of a slot s. ${C_s}^0 is empty for every slot. Limit$ {C_s}^t  to K.
• Take the candidates for a slot from user utterance, then system utterances and then from the past conversation based on scores (in decreasing order) until you hit K.

State Representation

• To ${C_s}^t$, we add 2 values - null and don’t care. This is the total set of possible values a slot can take.
• We also PAD to keep K constant.

Model Description

• DST is a discriminative model which takes the candidate set for each slot and assigns a score to each of them.
• Feature Extraction
• 3 types of features - utterance, slot and candidate.
• Delexicalization - substituting all the values of slot s with some function delex(s). Just the slot values and not slot names.
• Delexicalized utterance passed through 2-layers of BiLstm and we take the final hidden layer as the utterance representation.
• Utterance related features
• User Utterance and System Utterance.
• User dialog act and System dialog act (global ones ‘greetings’ and ‘negate’ only).
• Slot related features
• Relevant to a particular slot and are common across all candidates for the slot.
• Features - Binary vectors indicating the presence of slot specific dialog act (request or deny) for both user and system. Also include scores of null and don’t care in the previous turn.
• Candidate related features
• Features - Binary vectors indicating the presence of candidate specific dialog act (inform, modify) and the score of candidate in prev turn.
• Also use the utterance feature from the LSTM.

### Results

Paper Accuracy on DSTC2
This paper 0.703
Rule-based Baseline 0.619