DeepAI
Log In Sign Up

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight

04/06/2020
by   Hengyi Cai, et al.
9

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear. This impedes the learning of those data-driven neural dialogue models. Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples. In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously. In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data. Note that, the proposed data manipulation framework is fully data-driven and learnable. It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples. Extensive experiments show that our framework can improve the dialogue generation performance with respect to 13 automatic evaluation metrics and human judgments.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/02/2020

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven ...
04/21/2022

A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation

Towards building intelligent dialogue agents, there has been a growing i...
01/27/2017

Adversarial Evaluation of Dialogue Models

The recent application of RNN encoder-decoder models has resulted in sub...
09/27/2018

NEXUS Network: Connecting the Preceding and the Following in Dialogue Generation

Sequence-to-Sequence (seq2seq) models have become overwhelmingly popular...
09/29/2022

Effective Vision Transformer Training: A Data-Centric Perspective

Vision Transformers (ViTs) have shown promising performance compared wit...
12/17/2015

A Survey of Available Corpora for Building Data-Driven Dialogue Systems

During the past decade, several areas of speech and language understandi...
09/14/2021

Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization

Being able to reply with a related, fluent, and informative response is ...