Semantic text similarity plays an important role in software engineering
tasks in which engineers are requested to clarify the semantics of descriptive
labels (e.g., business terms, table column names) that are often consists of
too short or too generic words and appears in their IT systems. We formulate
this type of problem as a task of matching descriptive labels to glossary
descriptions. We then propose a framework to leverage an existing semantic text
similarity measurement (STS) and augment it using semantic label enrichment and
set-based collective contextualization where the former is a method to retrieve
sentences relevant to a given label and the latter is a method to compute
similarity between two contexts each of which is derived from a set of texts
(e.g., column names in the same table). We performed an experiment on two
datasets derived from publicly available data sources. The result indicated
that the proposed methods helped the underlying STS correctly match more
descriptive labels with the descriptions.

语义文本相似性在软件工程中具有重要作用，在工程师需要澄清描述性标签（例如，业务术语、表列名）的语义的任务中，这些标签通常由过于简短或过于通用的词组成，并在其信息技术系统中出现。我们将这种问题定义为将描述性标签与词汇表描述匹配的任务。然后，我们提出了一个框架，利用现有的语义文本相似性测量（STS），并使用语义标签增强和基于集合的上下文化来增强它，其中前者是一种检索与给定标签相关的句子的方法，而后者是一种计算两个上下文之间相似度的方法，其中每个上下文均来自一组文本（例如，同一表中的列名）。我们对两个由公开可用数据源生成的数据集进行了实验，结果表明，所提出的方法能够帮助底层 STS 正确匹配更多的描述性标签与描述。

描述标签与术语描述的匹配

Matching of Descriptive Labels to Glossary Descriptions

Contrastive learning has been extensively studied in sentence embedding
learning, which assumes that the embeddings of different views of the same
sentence are closer. The constraint brought by this assumption is weak, and a
good sentence representation should also be able to reconstruct the original
sentence fragments. Therefore, this paper proposes an information-aggregated
contrastive learning framework for learning unsupervised sentence embeddings,
termed InfoCSE. InfoCSE forces the representation of [CLS] positions to
aggregate denser sentence information by introducing an additional Masked
language model task and a well-designed network. We evaluate the proposed
InfoCSE on several benchmark datasets w.r.t the semantic text similarity (STS)
task. Experimental results show that InfoCSE outperforms SimCSE by an average
Spearman correlation of 2.60% on BERT-base, and 1.77% on BERT-large, achieving
state-of-the-art results among unsupervised sentence representation learning
methods. Our code are available at
this https URL.

本文提出了一种信息聚合的对比学习框架 InfoCSE 用于学习无监督的句子嵌入，该框架引入了一个额外的掩蔽语言模型任务和一个经过精心设计的网络来强制 [CLS] 位置的表示聚合更密集的句子信息，并在语义文本相似性（STS）任务上取得了超越 SimCSE 的表现，平均 Spearman 相关性增加了 2.60％（BERT-base）和 1.77％（BERT-large），成为无监督句子表示学习方法中的最新成果。

InfoCSE: 句子嵌入的信息聚合对比学习

InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings

This paper presents the shared task on Multilingual Idiomaticity Detection
and Sentence Embedding, which consists of two subtasks: (a) a binary
classification task aimed at identifying whether a sentence contains an
idiomatic expression, and (b) a task based on semantic text similarity which
requires the model to adequately represent potentially idiomatic expressions in
context. Each subtask includes different settings regarding the amount of
training data. Besides the task description, this paper introduces the datasets
in English, Portuguese, and Galician and their annotation procedure, the
evaluation metrics, and a summary of the participant systems and their results.
The task had close to 100 registered participants organised into twenty five
teams making over 650 and 150 submissions in the practice and evaluation phases
respectively.

这篇论文介绍了跨语种成语识别和句子嵌入的共享任务，包括二分类任务和基于文本相似度的任务，并说明了训练数据、数据集、评估指标、参与系统和结果。这项任务吸引了近 100 个注册参与者，成立了 25 个团队，在实践和评估阶段共提交了超过 650 和 150 份提交 respectively。