Generating stylized responses is essential to build intelligent and engaging
dialogue systems. However, this task is far from well-explored due to the
difficulties of rendering a particular style in coherent responses, especially
when the target style is embedded only in unpaired texts that cannot be
directly used to train the dialogue model. This paper proposes a stylized
dialogue generation method that can capture stylistic features embedded in
unpaired texts. Specifically, our method can produce dialogue responses that
are both coherent to the given context and conform to the target style. In this
study, an inverse dialogue model is first introduced to predict possible posts
for the input responses, and then this inverse model is used to generate
stylized pseudo dialogue pairs based on these stylized unpaired texts. Further,
these pseudo pairs are employed to train the stylized dialogue model with a
joint training process, and a style routing approach is proposed to intensify
stylistic features in the decoder. Automatic and manual evaluations on two
datasets demonstrate that our method outperforms competitive baselines in
producing coherent and style-intensive dialogue responses.

本研究提出一种基于逆向对话模型和风格路由方法的对话生成技术，能够捕捉嵌入在不成对文本中的风格特征，产生既符合给定上下文又符合目标风格的对话回复，实验表明方法能够胜过竞争性对话系统在产生连贯、风格强烈的对话回复方面的表现。

使用风格化非成对文本生成风格化对话回应

Stylized Dialogue Response Generation Using Stylized Unpaired Texts

Dialogue systems pretrained with large language models generate locally
coherent responses, but lack the fine-grained control over responses necessary
to achieve specific goals. A promising method to control response generation is
exemplar-based generation, in which models edit exemplar responses that are
retrieved from training data, or hand-written to strategically address
discourse-level goals, to fit new dialogue contexts. But, current
exemplar-based approaches often excessively copy words from the exemplar
responses, leading to incoherent replies. We present an Exemplar-based Dialogue
Generation model, EDGE, that uses the semantic frames present in exemplar
responses to guide generation. We show that controlling dialogue generation
based on the semantic frames of exemplars, rather than words in the exemplar
itself, improves the coherence of generated responses, while preserving
semantic meaning and conversation goals present in exemplar responses.

使用基于语义框架的示例响应指导，控制对话生成提高了生成响应的连贯性，同时保留了示例响应中存在的语义意义和对话目标。

用语义例子控制对话生成

Controlling Dialogue Generation with Semantic Exemplars

Endowing dialogue systems with personas is essential to deliver more
human-like conversations. However, this problem is still far from well explored
due to the difficulties of both embodying personalities in natural languages
and the persona sparsity issue observed in most dialogue corpora. This paper
proposes a pre-training based personalized dialogue model that can generate
coherent responses using persona-sparse dialogue data. In this method, a
pre-trained language model is used to initialize an encoder and decoder, and
personal attribute embeddings are devised to model richer dialogue contexts by
encoding speakers' personas together with dialogue histories. Further, to
incorporate the target persona in the decoding process and to balance its
contribution, an attention routing structure is devised in the decoder to merge
features extracted from the target persona and dialogue contexts using
dynamically predicted weights. Our model can utilize persona-sparse dialogues
in a unified manner during the training process, and can also control the
amount of persona-related features to exhibit during the inference process.
Both automatic and manual evaluation demonstrates that the proposed model
outperforms state-of-the-art methods for generating more coherent and persona
consistent responses with persona-sparse data.

本文提出了一种基于预训练的个性化对话模型，利用个人属性嵌入来丰富对话语境，结合注意力路由结构进行解码，从而生成更为连贯且人物一致的回答。

基于预训练的个性化对话生成模型及面向稀疏信息处理

A Pre-training Based Personalized Dialogue Generation Model with  Persona-sparse Data

Generating semantically coherent responses is still a major challenge in
dialogue generation. Different from conventional text generation tasks, the
mapping between inputs and responses in conversations is more complicated,
which highly demands the understanding of utterance-level semantic dependency,
a relation between the whole meanings of inputs and outputs. To address this
problem, we propose an Auto-Encoder Matching (AEM) model to learn such
dependency. The model contains two auto-encoders and one mapping module. The
auto-encoders learn the semantic representations of inputs and responses, and
the mapping module learns to connect the utterance-level representations.
Experimental results from automatic and human evaluations demonstrate that our
model is capable of generating responses of high coherence and fluency compared
to baseline models. The code is available at this https URL

提出一种基于自动编码器匹配的模型，用于学习对话生成任务中输入和输出的语义依赖关系，实现生成有逻辑性、流畅性的回复。