Neural chat translation (NCT) aims to translate a cross-lingual chat between
speakers of different languages. Existing context-aware NMT models cannot
achieve satisfactory performances due to the following inherent problems: 1)
limited resources of annotated bilingual dialogues; 2) the neglect of modelling
conversational properties; 3) training discrepancy between different stages. To
address these issues, in this paper, we propose a multi-task multi-stage
transitional (MMT) training framework, where an NCT model is trained using the
bilingual chat translation dataset and additional monolingual dialogues. We
elaborately design two auxiliary tasks, namely utterance discrimination and
speaker discrimination, to introduce the modelling of dialogue coherence and
speaker characteristic into the NCT model. The training process consists of
three stages: 1) sentence-level pre-training on large-scale parallel corpus; 2)
intermediate training with auxiliary tasks using additional monolingual
dialogues; 3) context-aware fine-tuning with gradual transition. Particularly,
the second stage serves as an intermediate phase that alleviates the training
discrepancy between the pre-training and fine-tuning stages. Moreover, to make
the stage transition smoother, we train the NCT model using a gradual
transition strategy, i.e., gradually transiting from using monolingual to
bilingual dialogues. Extensive experiments on two language pairs demonstrate
the effectiveness and superiority of our proposed training framework.

本论文旨在研究神经聊天翻译 (NCT)，针对已有上下文感知机器翻译模型存在的问题，提出了一种多任务多阶段过渡 (MMT) 训练框架，采用双语聊天翻译数据集和额外的单语对话训练 NCT 模型，并引入话语一致性和说话人特征建模。训练过程包括三个阶段，通过逐步转换单语为双语对话，MMT 能够有效地缓解训练阶段的差异并提高翻译效果。

神经聊天翻译的多任务多阶段转移训练框架

A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation

Target speech extraction, which extracts a single target source in a mixture
given clues about the target speaker, has attracted increasing attention. We
have recently proposed SpeakerBeam, which exploits an adaptation utterance of
the target speaker to extract his/her voice characteristics that are then used
to guide a neural network towards extracting speech of that speaker.
SpeakerBeam presents a practical alternative to speech separation as it enables
tracking speech of a target speaker across utterances, and achieves promising
speech extraction performance. However, it sometimes fails when speakers have
similar voice characteristics, such as in same-gender mixtures, because it is
difficult to discriminate the target speaker from the interfering speakers. In
this paper, we investigate strategies for improving the speaker discrimination
capability of SpeakerBeam. First, we propose a time-domain implementation of
SpeakerBeam similar to that proposed for a time-domain audio separation network
(TasNet), which has achieved state-of-the-art performance for speech
separation. Besides, we investigate (1) the use of spatial features to better
discriminate speakers when microphone array recordings are available, (2)
adding an auxiliary speaker identification loss for helping to learn more
discriminative voice characteristics. We show experimentally that these
strategies greatly improve speech extraction performance, especially for
same-gender mixtures, and outperform TasNet in terms of target speech
extraction.

本文提出 SpeakerBeam 算法实现对目标说话人的识别和提取，对同性别混合语音的语音分离效果不如期望，但在本文中提出的时间域、空间特征和辅助说话人识别方法使得提取效果得到增强并超过了 TasNet。