# Atom Responding Machine for Dialog Generation

## TL;DR

The paper introduces the Atom Responding Machine (ARM), a novel encoder-composer-decoder network that enhances dialogue response diversity and relevance by modeling multiple responding styles through atom and molecule mechanisms, with demonstrated improved response quality and interpretability.

## Contribution

It proposes ARM, a new model that uses atom and molecule mechanisms within an encoder-composer-decoder framework to generate diverse, relevant dialogue responses with interpretability.

## Key findings

- ARM improves response diversity and quality.
- The model demonstrates interpretability in response generation.
- Experimental results confirm effectiveness over conventional models.

## Abstract

Recently, improving the relevance and diversity of dialogue system has attracted wide attention. For a post x, the corresponding response y is usually diverse in the real-world corpus, while the conventional encoder-decoder model tends to output the high-frequency (safe but trivial) responses and thus is difficult to handle the large number of responding styles. To address these issues, we propose the Atom Responding Machine (ARM), which is based on a proposed encoder-composer-decoder network trained by a teacher-student framework. To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms. In other words, even a little of atom-mechanisms can make a mickle of molecule-mechanisms. The experiments demonstrate diversity and quality of the responses generated by ARM. We also present generating process to show underlying interpretability for the result.