DeepAI
Log In Sign Up

Parallel Interactive Networks for Multi-Domain Dialogue State Generation

09/16/2020
by   Junfan Chen, et al.
0

The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multi-domain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.

READ FULL TEXT

page 1

page 2

page 3

page 4

09/16/2020

Neural Dialogue State Tracking with Temporally Expressive Networks

Dialogue state tracking (DST) is an important part of a spoken dialogue ...
04/18/2018

Learning to Map Context-Dependent Sentences to Executable Formal Queries

We propose a context-dependent model to map utterances within an interac...
07/25/2021

Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking

Recently, researchers have explored using the encoder-decoder framework ...
12/01/2022

IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection

The task of response selection in multi-turn dialogue is to find the bes...
08/13/2020

Ranking Enhanced Dialogue Generation

How to effectively utilize the dialogue history is a crucial problem in ...
02/22/2020

Data Augmentation for Copy-Mechanism in Dialogue State Tracking

While several state-of-the-art approaches to dialogue state tracking (DS...

Code Repositories

PIN_EMNLP2020

The code and data for EMNLP 2020 paper: Parallel Interactive Networks for Multi-Domain Dialogue State Generation


view repo