A fundamental challenge in the current NLP context, dominated by language
models, comes from the inflexibility of current architectures to 'learn' new
information. While model-centric solutions like continual learning or
parameter-efficient fine tuning are available, the question still remains of
how to reliably identify changes in language or in the world. In this paper, we
propose WikiTiDe, a dataset derived from pairs of timestamped definitions
extracted from Wikipedia. We argue that such resource can be helpful for
accelerating diachronic NLP, specifically, for training models able to scan
knowledge resources for core updates concerning a concept, an event, or a named
entity. Our proposed end-to-end method is fully automatic, and leverages a
bootstrapping algorithm for gradually creating a high-quality dataset. Our
results suggest that bootstrapping the seed version of WikiTiDe leads to better
fine-tuned models. We also leverage fine-tuned models in a number of downstream
tasks, showing promising results with respect to competitive baselines.

本文介绍了 WikiTiDe 数据集，该数据集通过从维基百科提取的带时间戳的定义对创建而来。我们提出了完全自动的端到端方法，并利用自举算法逐步创建高质量数据集。研究结果表明，自举种子版本的 WikiTiDe 可以得到更好的微调模型，并在多个下游任务中取得了有竞争力的结果。

WIKITIDE: 基于维基百科的时间戳定义对数据集

WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset

Generating the English transliteration of a name written in a foreign script
is an important and challenging step in multilingual knowledge acquisition and
information extraction. Existing approaches to transliteration generation
require a large (>5000) number of training examples. This difficulty contrasts
with transliteration discovery, a somewhat easier task that involves picking a
plausible transliteration from a given list. In this work, we present a
bootstrapping algorithm that uses constrained discovery to improve generation,
and can be used with as few as 500 training examples, which we show can be
sourced from annotators in a matter of hours. This opens the task to languages
for which large number of training examples are unavailable. We evaluate
transliteration generation performance itself, as well the improvement it
brings to cross-lingual candidate generation for entity linking, a typical
downstream task. We present a comprehensive evaluation of our approach on nine
languages, each written in a unique script.

本篇论文介绍了一种用于改进多语言知识获取和信息提取中名字英语音译生成的算法，该算法可以用于许多缺乏大量训练示例的语言，并提高了候选生成对实体链接的贡献，作者在九种语言上进行了全面的评估。

用有限发现约束的引导法促进低资源语言的音译

Bootstrapping Transliteration with Constrained Discovery for  Low-Resource Languages

In order to assist security analysts in obtaining information pertaining to
their network, such as novel vulnerabilities, exploits, or patches, information
retrieval methods tailored to the security domain are needed. As labeled text
data is scarce and expensive, we follow developments in semi-supervised Natural
Language Processing and implement a bootstrapping algorithm for extracting
security entities and their relationships from text. The algorithm requires
little input data, specifically, a few relations or patterns (heuristics for
identifying relations), and incorporates an active learning component which
queries the user on the most important decisions to prevent drifting from the
desired relations. Preliminary testing on a small corpus shows promising
results, obtaining precision of .82.

本论文介绍了一种利用半监督自然语言处理和引导式算法从文本中提取安全实体与其关系的方法，并在小型语料库上进行测试，结果表明可以较高准确地实现此目的。