Dialog systems have achieved significant progress and have been widely used
in various scenarios. The previous researches mainly focused on designing
dialog generation models in a single scenario, while comprehensive abilities
are required to handle tasks under various scenarios in the real world. In this
paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which
can be applied in different dialog tasks (e.g. knowledge grounded dialog and
persona based dialog). Specifically, we propose a transferable response
generator pre-trained on diverse large-scale dialog corpora as the backbone of
MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the
response consistent with dialog history, we propose a consistency selector
trained through negative sampling. Moreover, the flexible copy mechanism of
external knowledge is also employed to enhance the utilization of multiform
knowledge in various scenarios. We conduct experiments on knowledge grounded
dialog, recommendation dialog, and persona based dialog tasks. The experimental
results indicate that our MSDF outperforms the baseline models with a large
margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge,
our general MSDF won the 3rd prize, which proves our MSDF is effective and
competitive.

该研究提出了一种通用的多技能对话框架 MSDF, 它可以应用于不同的对话任务，并通过在不同的大型对话语料库上预训练响应生成器来实现。使用外部知识的灵活复制机制强化了在各种情境中的多种形式知识的利用，并且在多项任务中较基准模型表现更好。

MSDF: 通用开放域多技能对话框架

MSDF: A General Open-Domain Multi-Skill Dialog Framework

This paper is concerned with dialogue state tracking (DST) in a task-oriented
dialogue system. Building a DST module that is highly effective is still a
challenging issue, although significant progresses have been made recently.
This paper proposes a new approach to dialogue state tracking, referred to as
Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU
employs two BERT-based encoders to respectively encode the utterances in the
dialogue and the descriptions of schemas, an attender to calculate attentions
between the utterance embeddings and the schema embeddings, and a decoder to
generate pointers to represent the current state of dialogue. Seq2Seq-DU has
the following advantages. It can jointly model intents, slots, and slot values;
it can leverage the rich representations of utterances and schemas based on
BERT; it can effectively deal with categorical and non-categorical slots, and
unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural
language understanding) module of a dialogue system. Experimental results on
benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1,
WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the
existing methods.

该研究论文提出了一种新的对话状态追踪方法，使用 Seq2Seq-DU，该方法通过两个基于 BERT 的编码器来编码对话中的话语和模式的描述，可以有效地处理类别和非类别槽，并能够联合建模意图、槽和槽值，其效果较现有方法更为优越。