In real dialogue scenarios, the existing slot filling model, which tends to
memorize entity patterns, has a significantly reduced generalization facing
Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV
robust slot filling model based on multi-level data augmentations to solve the
OOV problem from both word and slot perspectives. We present a unified
contrastive learning framework, which pull representations of the origin sample
and augmentation samples together, to make the model resistant to OOV problems.
We evaluate the performance of the model from some specific slots and carefully
design test data with OOV word perturbation to further demonstrate the
effectiveness of OOV words. Experiments on two datasets show that our approach
outperforms the previous sota methods in terms of both OOV slots and words.

本文介绍了一种基于多层数据扩增的 OOV 坚韧性槽填充模型，通过从词和槽的角度解决 OOV 问题。实验表明，该模型比现有模型和之前的最佳模型在 OOV 词和槽方面表现更出色。

针对槽位填充的词汇外问题的再探：一种综合对比框架，配有多级数据增强

Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations

In the following report we propose pipelines for Goodness of Pronunciation
(GoP) computation solving OOV problem at testing time using Vocab/Lexicon
expansion techniques. The pipeline uses different components of ASR system to
quantify accent and automatically evaluate them as scores. We use the
posteriors of an ASR model trained on native English speech, along with the
phone level boundaries to obtain phone level pronunciation scores. We used this
as a baseline pipeline and implemented methods to remove UNK and SPN phonemes
in the GoP output by building three pipelines. The Online, Offline and Hybrid
pipeline which returns the scores but also can prevent unknown words in the
final output. The Online method is based per utterance, Offline method
pre-incorporates a set of OOV words for a given data set and the Hybrid method
combines the above two ideas to expand the lexicon as well work per utterance.
We further provide utilities such as the Phoneme to posterior mappings, GoP
scores of each utterance as a vector, and Word boundaries used in the GoP
pipeline for use in future research.

通过使用 ASR 系统的不同组件评估口音并自动评估它们，我们提出了一种解决测试时开放式语音识别系统中的 OOV 问题的 GoP 计算的流水线，并且通过构建三个管道来实现去除 GoP 输出中的 UNK 和 SPN 音素，Online、Offline 和 Hybrid 管道，以扩展词库以及对每个 utterance 进行处理，最终提供 Phoneme to posterior mappings、每个 utterance 的 GoP 得分向量以及 GoP 流程中使用的 Word boundaries 等实用工具供未来研究使用。

解决 OOV 问题的发音流水线的优良性

Goodness of Pronunciation Pipelines for OOV Problem

Neural Machine Translation (NMT) is an open vocabulary problem. As a result,
dealing with the words not occurring during training (a.k.a. out-of-vocabulary
(OOV) words) have long been a fundamental challenge for NMT systems. The
predominant method to tackle this problem is Byte Pair Encoding (BPE) which
splits words, including OOV words, into sub-word segments. BPE has achieved
impressive results for a wide range of translation tasks in terms of automatic
evaluation metrics. While it is often assumed that by using BPE, NMT systems
are capable of handling OOV words, the effectiveness of BPE in translating OOV
words has not been explicitly measured. In this paper, we study to what extent
BPE is successful in translating OOV words at the word-level. We analyze the
translation quality of OOV words based on word type, number of segments,
cross-attention weights, and the frequency of segment n-grams in the training
data. Our experiments show that while careful BPE settings seem to be fairly
useful in translating OOV words across datasets, a considerable percentage of
OOV words are translated incorrectly. Furthermore, we highlight the slightly
higher effectiveness of BPE in translating OOV words for special cases, such as
named-entities and when the languages involved are linguistically close to each
other.

本文针对 NMT 中 OOV 问题进行研究，发现尽管 BPE 可在一定程度上处理 OOV，但仍有相当一部分单词翻译错误，特别是涉及语言间距离较远的情况。

Byte Pair 编码在神经机器翻译中的未登录词效果如何？

How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?

Lexicon-free speech recognition naturally deals with the problem of
out-of-vocabulary (OOV) words. In this paper, we show that character-based
language models (LM) can perform as well as word-based LMs for speech
recognition, in word error rates (WER), even without restricting the decoding
to a lexicon. We study character-based LMs and show that convolutional LMs can
effectively leverage large (character) contexts, which is key for good speech
recognition performance downstream. We specifically show that the lexicon-free
decoding performance (WER) on utterances with OOV words using character-based
LMs is better than lexicon-based decoding, both with character or word-based
LMs.

本文研究了基于字符的语言模型在无需限制解码字典情况下，进行语音识别表现与基于单词的模型相当，尤其通过利用卷积模型对大（字符）背景的有效使用，在处理具有 OOV 单词的语音识别中，lexicon-free 字符模型的表现优于基于字符或单词 LM 的字典识别。