Deep biasing (DB) improves the performance of end-to-end automatic speech
recognition (E2E-ASR) for rare words or contextual phrases using a bias list.
However, most existing methods treat bias phrases as sequences of subwords in a
predefined static vocabulary, which can result in ineffective learning of the
dependencies between subwords. More advanced techniques address this problem by
incorporating additional text data, which increases the overall workload. This
paper proposes a dynamic vocabulary where phrase-level bias tokens can be added
during the inference phase. Each bias token represents an entire bias phrase
within a single token, thereby eliminating the need to learn the dependencies
between the subwords within the bias phrases. This method can be applied to
various architectures because it only extends the embedding and output layers
in common E2E-ASR architectures. Experimental results demonstrate that the
proposed method improves the performance of bias phrases on English and
Japanese datasets.

通过使用动态词汇表中的短语级别偏置令牌，提出的方法改善了英语和日语数据集上偏置短语在端到端自动语音识别中的性能。

上下文化动态词汇的自动语音识别

Contextualized Automatic Speech Recognition with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.

本研究提出了一种神经机器翻译（NMT）模型间的知识转移方法，通过共享动态词汇表来适应新语言，以达到提高翻译性能的目的，并在多种语言方向上进行了实验，结果表明具有良好的性能。

多语言神经机器翻译中的迁移学习和动态词汇

Transfer Learning in Multilingual Neural Machine Translation with  Dynamic Vocabulary

We study response generation for open domain conversation in chatbots.
Existing methods assume that words in responses are generated from an identical
vocabulary regardless of their inputs, which not only makes them vulnerable to
generic patterns and irrelevant noise, but also causes a high cost in decoding.
We propose a dynamic vocabulary sequence-to-sequence (DVS2S) model which allows
each input to possess their own vocabulary in decoding. In training, vocabulary
construction and response generation are jointly learned by maximizing a lower
bound of the true objective with a Monte Carlo sampling method. In inference,
the model dynamically allocates a small vocabulary for an input with the word
prediction model, and conducts decoding only with the small vocabulary. Because
of the dynamic vocabulary mechanism, DVS2S eludes many generic patterns and
irrelevant words in generation, and enjoys efficient decoding at the same time.
Experimental results on both automatic metrics and human annotations show that
DVS2S can significantly outperform state-of-the-art methods in terms of
response quality, but only requires 60% decoding time compared to the most
efficient baseline.

本文研究聊天机器人开放领域的回答生成。我们提出了一种动态词汇序列到序列（DVS2S）模型，它允许每个输入在解码时拥有自己的词汇。DVS2S 避免了许多通用模式和无关单词的生成，并同时享有高效的解码。实验结果表明 DVS2S 的回答质量明显优于最先进的方法，但仅需要 60％的解码时间。