Open-domain dialogue systems aim to interact with humans through natural
language texts in an open-ended fashion. Despite the recent success of super
large dialogue systems such as ChatGPT, using medium-to-small-sized dialogue
systems remains the common practice as they are more lightweight and
accessible; however, generating diverse dialogue responses is challenging,
especially with smaller models. In this work, we propose an Equal-size Hard
Expectation--Maximization (EqHard-EM) algorithm to train a multi-decoder model
for diverse dialogue generation. Our algorithm assigns a sample to a decoder in
a hard manner and additionally imposes an equal-assignment constraint to ensure
that all decoders are well-trained. We provide detailed theoretical analysis to
justify our approach. Further, experiments on two large-scale open-domain
dialogue datasets verify that our EqHard-EM algorithm generates high-quality
diverse responses.

本文提出了一种 Equal-size Hard Expectation--Maximization 有算法，用于在多个解码器模型中生成多样性会话响应，理论分析和实验结果表明该算法能够生成高质量的多样化响应。

一种用于多样化对话生成的等尺寸 Hard EM 算法

An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation

End-to-end approaches for sequence tasks are becoming increasingly popular.
Yet for complex sequence tasks, like speech translation, systems that cascade
several models trained on sub-tasks have shown to be superior, suggesting that
the compositionality of cascaded systems simplifies learning and enables
sophisticated search capabilities. In this work, we present an end-to-end
framework that exploits compositionality to learn searchable hidden
representations at intermediate stages of a sequence model using decomposed
sub-tasks. These hidden intermediates can be improved using beam search to
enhance the overall performance and can also incorporate external models at
intermediate stages of the network to re-score or adapt towards out-of-domain
data. One instance of the proposed framework is a Multi-Decoder model for
speech translation that extracts the searchable hidden intermediates from a
speech recognition sub-task. The model demonstrates the aforementioned benefits
and outperforms the previous state-of-the-art by around +6 and +3 BLEU on the
two test sets of Fisher-CallHome and by around +3 and +4 BLEU on the
English-German and English-French test sets of MuST-C.

我们提出了一个利用分解子任务进行可搜索的中间隐层表示学习的端到端框架，在语音翻译等复杂序列任务上实现了优越的性能。