# Improving Neural Conversational Models with Entropy-Based Data Filtering

## TL;DR

This paper introduces an entropy-based data filtering method to improve neural conversational models by removing generic responses, leading to more diverse and engaging chatbot outputs without requiring manual annotations.

## Contribution

The authors propose a simple, unsupervised entropy-based filtering technique to enhance response diversity in neural dialog models, outperforming previous methods that rely on model modifications or annotated data.

## Key findings

- Filtered datasets produce more diverse responses.
- Models trained on filtered data outperform baselines on 17 metrics.
- The method is effective without human supervision.

## Abstract

Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. Priors such as persona, emotion, or topic provide additional information to dialog models to aid response generation, but annotating a dataset with priors is expensive and such annotations are rarely available. While previous methods for improving the quality of open-domain response generation focused on either the underlying model or the training objective, we present a method of filtering dialog datasets by removing generic utterances from training data using a simple entropy-based approach that does not require human supervision. We conduct extensive experiments with different variations of our method, and compare dialog models across 17 evaluation metrics to show that training on datasets filtered this way results in better conversational quality as chatbots learn to output more diverse responses.