Human dialogue contains evolving concepts, and speakers naturally associate
multiple concepts to compose a response. However, current dialogue models with
the seq2seq framework lack the ability to effectively manage concept
transitions and can hardly introduce multiple concepts to responses in a
sequential decoding manner. To facilitate a controllable and coherent dialogue,
in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for
open-domain dialogue generation. The proposed model comprises a multi-concept
planning module that learns to identify multiple associated concepts from a
concept graph and a customized Insertion Transformer that performs
concept-guided non-autoregressive generation to complete a response. The
experimental results on two public datasets show that CG-nAR can produce
diverse and coherent responses, outperforming state-of-the-art baselines in
both automatic and human evaluations with substantially faster inference speed.

设计一个基于概念引导的非自回归模型，为了更好地管理概念转换并能够顺序解码引入多个概念，实现可控和连贯的对话生成，实验表明该模型在速度和质量上都优于现有最先进的模型。

清晰思考，快速交谈：面向开放域对话系统的概念引导非自回归生成

Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive  Generation for Open-Domain Dialogue Systems

We present a novel approach to any-to-one (A2O) voice conversion (VC) in a
sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker,
including those unseen during training, to a fixed target speaker. We utilize
vq-wav2vec (VQW2V), a discretized self-supervised speech representation that
was learned from massive unlabeled data, which is assumed to be
speaker-independent and well corresponds to underlying linguistic contents.
Given a training dataset of the target speaker, we extract VQW2V and acoustic
features to estimate a seq2seq mapping function from the former to the latter.
With the help of a pretraining method and a newly designed postprocessing
technique, our model can be generalized to only 5 min of data, even
outperforming the same model trained with parallel data.

利用自监督 VQ-Wav2vec 表示的任意人说话到固定目标说话的任意对一语音转换问题的序列到序列框架，通过预处理和后处理方法，我们的模型可以具有更好的泛化能力，即使只有 5 分钟的数据。

使用自监督离散语音表示进行任意一对多序列到序列语音转换

Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised  Discrete Speech Representations

Sequences have become first class citizens in supervised learning thanks to
the resurgence of recurrent neural networks. Many complex tasks that require
mapping from or to a sequence of observations can now be formulated with the
sequence-to-sequence (seq2seq) framework which employs the chain rule to
efficiently represent the joint probability of sequences. In many cases,
however, variable sized inputs and/or outputs might not be naturally expressed
as sequences. For instance, it is not clear how to input a set of numbers into
a model where the task is to sort them; similarly, we do not know how to
organize outputs when they correspond to random variables and the task is to
model their unknown joint probability. In this paper, we first show using
various examples that the order in which we organize input and/or output data
matters significantly when learning an underlying model. We then discuss an
extension of the seq2seq framework that goes beyond sequences and handles input
sets in a principled way. In addition, we propose a loss which, by searching
over possible orders during training, deals with the lack of structure of
output sets. We show empirical evidence of our claims regarding ordering, and
on the modifications to the seq2seq framework on benchmark language modeling
and parsing tasks, as well as two artificial tasks -- sorting numbers and
estimating the joint probability of unknown graphical models.

本文扩展了 seq2seq 模型，实现了对输入集的合理处理以及通过搜索可能的排序来处理输出集的缺乏结构，并提出相应的解决方案，最终在多项自然语言处理任务和两项人工任务中取得了良好的结果。