Crosstalk is a traditional Chinese theatrical performance art. It is commonly
performed by two performers in the form of a dialogue. With the typical
features of dialogues, crosstalks are also designed to be hilarious for the
purpose of amusing the audience. In this study, we introduce CrossDial, the
first open-source dataset containing most classic Chinese crosstalks crawled
from the Web. Moreover, we define two new tasks, provide two benchmarks, and
investigate the ability of current dialogue generation models in the field of
crosstalk generation. The experiment results and case studies demonstrate that
crosstalk generation is challenging for straightforward methods and remains an
interesting topic for future works.

本研究介绍了 CrossDial，它是来自 Web 的包含大多数经典中国相声的首个开源数据集。同时，我们定义了两个新任务，提供了两个基准，并调查了当前对话生成模型在相声生成领域中的能力。实验结果和案例研究表明，相声生成对于直接方法是有挑战性的，同时也是未来工作的有趣研究方向。

CrossDial：一个有趣的相声对话中文数据集

CrossDial: An Entertaining Dialogue Dataset of Chinese Crosstalk

Conversations are always related to certain topics. However, it is
challenging to fuse dialogue history and topic information from various sources
at the same time in current dialogue generation models because of the input
length limit of pre-trained language models (PLMs). In order to expand the
information that PLMs can utilize, we encode topic and dialogue history
information using certain prompts with multiple channels of Fusion-in-Decoder
(FiD) and explore the influence of three different channel settings. In this
paper, our experiments focus on a specific Chinese dataset named NaturalConv,
where the conversation revolves around a piece of recent news. We thoroughly
compared different dialogue models and different FiD channel settings.
Empirical results show that by combining our proposed whole passage channel
with additional history channel, our methods can achieve competitive
performance on NaturalConv, making it possible to encode various information
from excessively long texts.

该研究通过 Fusion-in-Decoder 技术，采用多个通道对话历史和主题信息进行编码，实现了对长文本的多方面信息的利用，通过实验发现整篇文章通道加上历史通道的方法在 NaturalConv 数据集上的表现比较竞争性。

多轮主题驱动对话中有效信息利用的探究

Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms
or exposed to risk factors have a pressing need to consult doctors. Due to
hospital closure, a lot of consulting services have been moved online. Because
of the shortage of medical professionals, many people cannot receive online
consultations timely. To address this problem, we aim to develop a medical
dialogue system that can provide COVID19-related consultations. We collected
two dialogue datasets -- CovidDialog -- (in English and Chinese respectively)
containing conversations between doctors and patients about COVID-19. On these
two datasets, we train several dialogue generation models based on Transformer,
GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size,
which bear high risk of overfitting, we leverage transfer learning to mitigate
data deficiency. Specifically, we take the pretrained models of Transformer,
GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune
them on our CovidDialog tasks. We perform both automatic and human evaluation
of responses generated by these models. The results show that the generated
responses are promising in being doctor-like, relevant to the conversation
history, and clinically informative. The data and code are available at
this https URL

为了解决 COVID-19 期间在线咨询医生不及时的问题，我们开发了一种医学对话系统，该系统基于 Transformer，GPT 和 BERT-GPT 训练了几个对话生成模型，并使用迁移学习减轻数据缺乏的风险。自动和人工评估表明，这些模型生成的响应很有前途，类似于医生，并且与对话历史相关和具有临床信息。数据和代码可在提供的 https URL 中获得。