Emotional support is a crucial skill for many real-world scenarios, including
caring for the elderly, mental health support, and customer service chats. This
paper presents a novel task of empathetic dialog generation with positive
emotion elicitation to promote users' positive emotions, similar to that of
emotional support between humans. In this task, the agent conducts empathetic
responses along with the target of eliciting the user's positive emotions in
the multi-turn dialog. To facilitate the study of this task, we collect a
large-scale emotional dialog dataset with positive emotion elicitation, called
PosEmoDial (about 820k dialogs, 3M utterances). In these dialogs, the agent
tries to guide the user from any possible initial emotional state, e.g.,
sadness, to a positive emotional state. Then we present a
positive-emotion-guided dialog generation model with a novel loss function
design. This loss function encourages the dialog model to not only elicit
positive emotions from users but also ensure smooth emotional transitions along
with the whole dialog. Finally, we establish benchmark results on PosEmoDial,
and we will release this dataset and related source code to facilitate future
studies.

本文提出了一种新的任务：使用情感支持生成具有积极情感诱导的共情式对话，以促进用户情感的积极化。为了研究这一任务，我们收集了一个大规模的情感对话数据集 (PosEmoDial)，并建立了一个基准结果，同时我们将发布这个数据集和相关源代码以促进未来的研究。

多轮共情对话中的积极情感引导探究

Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation

There is an increasing demand for goal-oriented conversation systems which
can assist users in various day-to-day activities such as booking tickets,
restaurant reservations, shopping, etc. Most of the existing datasets for
building such conversation systems focus on monolingual conversations and there
is hardly any work on multilingual and/or code-mixed conversations. Such
datasets and systems thus do not cater to the multilingual regions of the
world, such as India, where it is very common for people to speak more than one
language and seamlessly switch between them resulting in code-mixed
conversations. For example, a Hindi speaking user looking to book a restaurant
would typically ask, "Kya tum is restaurant mein ek table book karne mein meri
help karoge?" ("Can you help me in booking a table at this restaurant?"). To
facilitate the development of such code-mixed conversation models, we build a
goal-oriented dialog dataset containing code-mixed conversations. Specifically,
we take the text from the DSTC2 restaurant reservation dataset and create
code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English
and Tamil-English. We also establish initial baselines on this dataset using
existing state of the art models. This dataset along with our baseline
implementations is made publicly available for research purposes.

为了促进多语言代码混合对话模型的开发，我们构建了一个包含代码混合对话的目标导向对话数据集，并在其中使用英语 - 印地语、英语 - 孟加拉语、英语 - 古吉拉特语和英语 - 泰米尔语的多语言版本。