Open-domain dialogue systems have made promising progress in recent years.
While the state-of-the-art dialogue agents are built upon large-scale
text-based social media data and large pre-trained models, there is no
guarantee these agents could also perform well in fast-growing scenarios, such
as live streaming, due to the bounded transferability of pre-trained models and
biased distributions of public datasets from Reddit and Weibo, etc. To improve
the essential capability of responding and establish a benchmark in the live
open-domain scenario, we introduce the LiveChat dataset, composed of 1.33
million real-life Chinese dialogues with almost 3800 average sessions across
351 personas and fine-grained profiles for each persona. LiveChat is
automatically constructed by processing numerous live videos on the Internet
and naturally falls within the scope of multi-party conversations, where the
issues of Who says What to Whom should be considered. Therefore, we target two
critical tasks of response modeling and addressee recognition and propose
retrieval-based baselines grounded on advanced techniques. Experimental results
have validated the positive effects of leveraging persona profiles and larger
average sessions per persona. In addition, we also benchmark the
transferability of advanced generation-based models on LiveChat and pose some
future directions for current challenges.

通过引入 1.33 亿真实的中文对话数据集 LiveChat，并提出基于先进技术的检索式基线方法，本研究在多方对话范围内针对回应建模和宾语识别等两个关键任务进行研究，并验证了利用人物特征和更大的平均会话数对任务的积极影响，同时还提出了当前挑战的未来方向。

LiveChat: 从直播流自动构建的大规模个性化对话数据集

LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically  Constructed from Live Streaming

Addressing the issues of who saying what to whom in multi-party conversations
(MPCs) has recently attracted a lot of research attention. However, existing
methods on MPC understanding typically embed interlocutors and utterances into
sequential information flows, or utilize only the superficial of inherent graph
structures in MPCs. To this end, we present a plug-and-play and lightweight
method named graph-induced fine-tuning (GIFT) which can adapt various
Transformer-based pre-trained language models (PLMs) for universal MPC
understanding. In detail, the full and equivalent connections among utterances
in regular Transformer ignore the sparse but distinctive dependency of an
utterance on another in MPCs. To distinguish different relationships between
utterances, four types of edges are designed to integrate graph-induced signals
into attention mechanisms to refine PLMs originally designed for processing
sequential texts. We evaluate GIFT by implementing it into three PLMs, and test
the performance on three downstream tasks including addressee recognition,
speaker identification and response selection. Experimental results show that
GIFT can significantly improve the performance of three PLMs on three
downstream tasks and two benchmarks with only 4 additional parameters per
encoding layer, achieving new state-of-the-art performance on MPC
understanding.

通过设计四种边将图诱导信号引入注意力机制，我们提出了一种名为图诱导微调 (GIFT) 的轻量级方法，可用于多方对话的普适性理解。通过将其应用到三个预训练的语言模型中，我们发现 GIFT 可以在三项下游任务中显着提高模型性能，仅每层编码增加 4 个参数，在多方对话理解领域实现了最新的最佳性能。