Since paraphrasing is an ill-defined task, the term "paraphrasing" covers
text transformation tasks with different characteristics. Consequently,
existing paraphrasing studies have applied quite different (explicit and
implicit) criteria as to when a pair of texts is to be considered a paraphrase,
all of which amount to postulating a certain level of semantic or lexical
similarity. In this paper, we conduct a literature review and propose a
taxonomy to organize the 25~identified paraphrasing (sub-)tasks. Using
classifiers trained to identify the tasks that a given paraphrasing instance
fits, we find that the distributions of task-specific instances in the known
paraphrase corpora vary substantially. This means that the use of these
corpora, without the respective paraphrase conditions being clearly defined
(which is the normal case), must lead to incomparable and misleading results.

通过文献综述和提出分类法，本研究对 25 个已知的改写（子）任务进行整理和组织。使用分类器识别给定改写实例适用的任务，发现已知改写语料库中特定任务实例的分布差异很大。这意味着在没有明确定义相应改写条件的情况下使用这些语料库会导致不可比较和误导性的结果。

任务导向的释义分析

Task-Oriented Paraphrase Analytics

This paper investigates the employment of various encoders in text
transformation, converting characters into bytes. It discusses local encoders
such as ASCII and GB-2312, which encode specific characters into shorter bytes,
and universal encoders like UTF-8 and UTF-16, which can encode the complete
Unicode set with greater space requirements and are gaining widespread
acceptance. Other encoders, including SCSU, BOCU-1, and binary encoders,
however, lack self-synchronizing capabilities. Duncode is introduced as an
innovative encoding method that aims to encode the entire Unicode character set
with high space efficiency, akin to local encoders. It has the potential to
compress multiple characters of a string into a Duncode unit using fewer bytes.
Despite offering less self-synchronizing identification information, Duncode
surpasses UTF8 in terms of space efficiency. The application is available at
\url{this https URL}. Additionally, we have developed a
benchmark for evaluating character encoders across different languages. It
encompasses 179 languages and can be accessed at
\url{this https URL}.

本文研究了各种编码器在文本转换中的应用，介绍了本地编码器和通用编码器，其中 Duncode 是一种创新的编码方法，旨在以高空间效率编码整个 Unicode 字符集，超越了 UTF8.

简化 Duncode 字符

Duncode Characters Shorter

Recent years have witnessed wider adoption of Automated Speech Recognition
(ASR) techniques in various domains. Consequently, evaluating and enhancing the
quality of ASR systems is of great importance. This paper proposes ASDF, an
Automated Speech Recognition Differential Testing Framework for testing ASR
systems. ASDF extends an existing ASR testing tool, the CrossASR++, which
synthesizes test cases from a text corpus. However, CrossASR++ fails to make
use of the text corpus efficiently and provides limited information on how the
failed test cases can improve ASR systems. To address these limitations, our
tool incorporates two novel features: (1) a text transformation module to boost
the number of generated test cases and uncover more errors in ASR systems and
(2) a phonetic analysis module to identify on which phonemes the ASR system
tend to produce errors. ASDF generates more high-quality test cases by applying
various text transformation methods (e.g., change tense) to the texts in failed
test cases. By doing so, ASDF can utilize a small text corpus to generate a
large number of audio test cases, something which CrossASR++ is not capable of.
In addition, ASDF implements more metrics to evaluate the performance of ASR
systems from multiple perspectives. ASDF performs phonetic analysis on the
identified failed test cases to identify the phonemes that ASR systems tend to
transcribe incorrectly, providing useful information for developers to improve
ASR systems. The demonstration video of our tool is made online at
this https URL. The implementation is available at
this https URL.

该论文提出了 ASDF（Automated Speech Recognition Differential Testing Framework）自动语音识别差异性测试框架，通过文本转换和音素分析模块生成更多高质量的音频测试用例，有助于改善 ASR 系统的性能。

ASDF：自动语音识别系统的差分测试框架

ASDF: A Differential Testing Framework for Automatic Speech Recognition Systems

The vast majority of text transformation techniques in NLP are inherently
limited in their ability to expand input space coverage due to an implicit
constraint to preserve the original class label. In this work, we propose the
notion of sibylvariance (SIB) to describe the broader set of transforms that
relax the label-preserving constraint, knowably vary the expected class, and
lead to significantly more diverse input distributions. We offer a unified
framework to organize all data transformations, including two types of SIB: (1)
Transmutations convert one discrete kind into another, (2) Mixture Mutations
blend two or more classes together. To explore the role of sibylvariance within
NLP, we implemented 41 text transformations, including several novel techniques
like Concept2Sentence and SentMix. Sibylvariance also enables a unique form of
adaptive training that generates new input mixtures for the most confused class
pairs, challenging the learner to differentiate with greater nuance. Our
experiments on six benchmark datasets strongly support the efficacy of
sibylvariance for generalization performance, defect detection, and adversarial
robustness.

本文提出了 sibylvariance 的概念，即在 NLP 中放宽标签约束的转换，包括 Transmutations 和 Mixture Mutations。通过实验验证发现，sibylvariances 在数据处理、缺陷检测和敌对鲁棒性等方面表现出了更好的普适性和效果。