This paper explores the use of text data augmentation techniques to enhance
conflict and duplicate detection in software engineering tasks through sentence
pair classification. The study adapts generic augmentation techniques such as
shuffling, back translation, and paraphrasing and proposes new data
augmentation techniques such as Noun-Verb Substitution, target-lemma
replacement and Actor-Action Substitution for software requirement texts. A
comprehensive empirical analysis is conducted on six software text datasets to
identify conflicts and duplicates among sentence pairs. The results demonstrate
that data augmentation techniques have a significant impact on the performance
of all software pair text datasets. On the other hand, in cases where the
datasets are relatively balanced, the use of augmentation techniques may result
in a negative effect on the classification performance.

本研究探讨使用文本数据增强技术如洗牌、反向翻译和改写等，以句对分类的方式增强软件工程任务中的冲突与重复检测效果。结论发现，数据增强技术对六个软件文本数据集的所有文本句对效果有显著影响，但在数据集相对平衡时，使用增强技术可能会对分类效果产生负面影响。

软件工程句对冲突和重复检测的数据增强

Data Augmentation for Conflict and Duplicate Detection in Software  Engineering Sentence Pairs

Monolingual word alignment is important for studying fine-grained editing
operations (i.e., deletion, addition, and substitution) in text-to-text
generation tasks, such as paraphrase generation, text simplification,
neutralizing biased language, etc. In this paper, we present a novel neural
semi-Markov CRF alignment model, which unifies word and phrase alignments
through variable-length spans. We also create a new benchmark with human
annotations that cover four different text genres to evaluate monolingual word
alignment models in more realistic settings. Experimental results show that our
proposed model outperforms all previous approaches for monolingual word
alignment as well as a competitive QA-based baseline, which was previously only
applied to bilingual data. Our model demonstrates good generalizability to
three out-of-domain datasets and shows great utility in two downstream
applications: automatic text simplification and sentence pair classification
tasks.

提供了一种新的神经半 - Markov CRF 对齐模型，它通过可变长度跨度统一了单词和短语对齐，通过人工注释创建了一个新的基准，显示出在现实设置下评估单语言对齐模型的性能， 并证明了该模型在自动文本简化和句对分类任务中具有良好的普适性和实用性。

神经半 - Markov CRF 用于单语词对齐

Neural semi-Markov CRF for Monolingual Word Alignment

The biases present in training datasets have been shown to affect models for
sentence pair classification tasks such as natural language inference (NLI) and
fact verification. While fine-tuning models on additional data has been used to
mitigate them, a common issue is that of catastrophic forgetting of the
original training dataset. In this paper, we show that elastic weight
consolidation (EWC) allows fine-tuning of models to mitigate biases while being
less susceptible to catastrophic forgetting. In our evaluation on fact
verification and NLI stress tests, we show that fine-tuning with EWC dominates
standard fine-tuning, yielding models with lower levels of forgetting on the
original (biased) dataset for equivalent gains in accuracy on the fine-tuning
(unbiased) dataset.

使用 EWC 精细调优可以减轻训练数据集中的偏见，同时对于原始训练数据集的遗忘率较低。