DeepAI
Log In Sign Up

A Simple But Effective Approach to n-shot Task-Oriented Dialogue Augmentation

02/27/2021
by   Taha Aksu, et al.
0

The collection and annotation of task-oriented conversational data is a costly and time-consuming manner. Many augmentation techniques have been proposed to improve the performance of state-of-the-art (SOTA) systems in new domains that lack the necessary amount of data for training. However, these augmentation techniques (e.g. paraphrasing) also require some mediocre amount of data since they use learning-based approaches. This makes using SOTA systems in emerging low-resource domains infeasible. We, to tackle this problem, introduce a framework, that creates synthetic task-oriented dialogues in a fully automatic manner, which operates with input sizes of as small as a few dialogues. Our framework uses the simple idea that each turn-pair in a task-oriented dialogue has a certain function and exploits this idea to mix them creating new dialogues. We evaluate our framework within a low-resource setting by integrating it with a SOTA model TRADE in the dialogue state tracking task and observe significant improvements in the fine-tuning scenarios in several domains. We conclude that this end-to-end dialogue augmentation framework can be a crucial tool for natural language understanding performance in emerging task-oriented dialogue domains.

READ FULL TEXT

page 1

page 4

03/15/2022

ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language

Most of the current task-oriented dialogue systems (ToD), despite having...
09/25/2020

MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems

In this paper, we propose Minimalist Transfer Learning (MinTL) to simpli...
09/16/2019

Domain Transfer in Dialogue Systems without Turn-Level Supervision

Task oriented dialogue systems rely heavily on specialized dialogue stat...
10/28/2020

Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation

Utterance classification performance in low-resource dialogue systems is...
10/12/2020

MultiWOZ 2.3: A multi-domain task-oriented dataset enhanced with annotation corrections and co-reference annotation

Task-oriented dialogue systems have made unprecedented progress with mul...
09/28/2020

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Task-oriented dialogue systems are either modularized with separate dial...
11/10/2022

Prompt Learning for Domain Adaptation in Task-Oriented Dialogue

Conversation designers continue to face significant obstacles when creat...