CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System

by   Etsuko Ishii, et al.

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.


page 1

page 2

page 3

page 4


Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models

The BioCreative VII Track 3 challenge focused on the identification of m...

Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue

The training of task-oriented dialogue systems is often confronted with ...

Persona-Knowledge Dialogue Multi-Context Retrieval and Enhanced Decoding Methods

Persona and Knowledge dual context open-domain chat is a novel dialogue ...

AuGPT: Dialogue with Pre-trained Language Models and Data Augmentation

Attention-based pre-trained language models such as GPT-2 brought consid...

Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent

Multiple sequence to sequence models were used to establish an end-to-en...

Scope of Pre-trained Language Models for Detecting Conflicting Health Information

An increasing number of people now rely on online platforms to meet thei...