This paper introduces a simple yet effective data-centric approach for the
task of improving persona-conditioned dialogue agents. Prior model-centric
approaches unquestioningly depend on the raw crowdsourced benchmark datasets
such as Persona-Chat. In contrast, we aim to fix annotation artifacts in
benchmarking, which is orthogonally applicable to any dialogue model.
Specifically, we augment relevant personas to improve dialogue dataset/agent,
by leveraging the primal-dual structure of the two tasks, predicting dialogue
responses and personas based on each other. Experiments on Persona-Chat show
that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of
accuracy.

本文介绍了一种简单而有效的数据中心方法，用于改善个性化对话代理。通过利用针对两个任务的原始 - 对偶结构（预测对话响应和个人资料之间的联系），我们增强了相关人物角色，以改进对话数据集 / 代理，并修复了基准数据集的注释问题，其巨大地增加了模型的精度，体现在 Persona-Chat 上的实验中，我们的方法在准确性上比预训练 LM 高出 11.7 个百分点。

改进角色感知对话数据集的双重任务框架

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

Despite the continuing efforts to improve the engagingness and consistency of
chit-chat dialogue systems, the majority of current work simply focus on
mimicking human-like responses, leaving understudied the aspects of modeling
understanding between interlocutors. The research in cognitive science,
instead, suggests that understanding is an essential signal for a high-quality
chit-chat conversation. Motivated by this, we propose P^2 Bot, a
transmitter-receiver based framework with the aim of explicitly modeling
understanding. Specifically, P^2 Bot incorporates mutual persona perception to
enhance the quality of personalized dialogue generation. Experiments on a large
public dataset, Persona-Chat, demonstrate the effectiveness of our approach,
with a considerable boost over the state-of-the-art baselines across both
automatic metrics and human evaluations.

本文提出了基于 P^2 框架的机器人 P^2 Bot，该框架旨在显式地建立对话者间的理解模型。实验结果表明该框架在大型公共数据集 Persona-Chat 上取得了显著效果提升。

通过相互人设感知实现的对话生成

You Impress Me: Dialogue Generation via Mutual Persona Perception

Personalized dialogue systems are an essential step toward better
human-machine interaction. Existing personalized dialogue agents rely on
properly designed conversational datasets, which are mostly monolingual (e.g.,
English), which greatly limits the usage of conversational agents in other
languages. In this paper, we propose a multi-lingual extension of Persona-Chat,
namely XPersona. Our dataset includes persona conversations in six different
languages other than English for building and evaluating multilingual
personalized agents. We experiment with both multilingual and cross-lingual
trained baselines, and evaluate them against monolingual and
translation-pipeline models using both automatic and human evaluation.
Experimental results show that the multilingual trained models outperform the
translation-pipeline and that they are on par with the monolingual models, with
the advantage of having a single model across multiple languages. On the other
hand, the state-of-the-art cross-lingual trained models achieve inferior
performance to the other models, showing that cross-lingual conversation
modeling is a challenging task. We hope that our dataset and baselines will
accelerate research in multilingual dialogue systems.

本研究提出了一个名为 XPersona 的个性化对话数据集，并通过多语言训练的基线模型和跨语言训练的基线模型进行了实验证明。结果表明，多语言训练的模型优于翻译管道模型，并且其性能与单语言训练的模型相当，而跨语言训练的模型则表现出劣于其他模型的性能。该数据集和基线模型有望加速多语言对话系统的研究。