  Being able to reply with a related, fluent, and informative response is an
indispensable requirement for building high-quality conversational agents. In
order to generate better responses, some approaches have been proposed, such as
feeding extra information by collecting large-scale datasets with human
annotations, designing neural conversational models (NCMs) with complex
architecture and loss functions, or filtering out untrustworthy samples based
on a dialogue attribute, e.g., Relatedness or Genericness. In this paper, we
follow the third research branch and present a data filtering method for
open-domain dialogues, which identifies untrustworthy samples from training
data with a quality measure that linearly combines seven dialogue attributes.
The attribute weights are obtained via Bayesian Optimization (BayesOpt) that
aims to optimize an objective function for dialogue generation iteratively on
the validation set. Then we score training samples with the quality measure,
sort them in descending order, and filter out those at the bottom. Furthermore,
to accelerate the "filter-train-evaluate" iterations involved in BayesOpt on
large-scale datasets, we propose a training framework that integrates maximum
likelihood estimation (MLE) and negative training method (NEG). The training
method updates parameters of a trained NCMs on two small sets with newly
maintained and removed samples, respectively. Specifically, MLE is applied to
maximize the log-likelihood of newly maintained samples, while NEG is used to
minimize the log-likelihood of newly removed ones. Experimental results on two
datasets show that our method can effectively identify untrustworthy samples,
and NCMs trained on the filtered datasets achieve better performance.
