Machine Translation is a mature technology for many high-resource language
pairs. However in the context of low-resource languages, there is a paucity of
parallel data datasets available for developing translation models.
Furthermore, the development of datasets for low-resource languages often
focuses on simply creating the largest possible dataset for generic
translation. The benefits and development of smaller in-domain datasets can
easily be overlooked. To assess the merits of using in-domain data, a dataset
for the specific domain of health was developed for the low-resource English to
Irish language pair. Our study outlines the process used in developing the
corpus and empirically demonstrates the benefits of using an in-domain dataset
for the health domain. In the context of translating health-related data,
models developed using the gaHealth corpus demonstrated a maximum BLEU score
improvement of 22.2 points (40%) when compared with top performing models from
the LoResMT2021 Shared Task. Furthermore, we define linguistic guidelines for
developing gaHealth, the first bilingual corpus of health data for the Irish
language, which we hope will be of use to other creators of low-resource data
sets. gaHealth is now freely available online and is ready to be explored for
further research.

机器翻译是许多高资源语言对的成熟技术，然而在低资源语言的情况下，缺乏用于开发翻译模型的平行数据集。本研究开发了针对低资源英语到爱尔兰语领域的特定健康领域的数据集，实验证明使用领域内数据集的好处。通过比较于 LoResMT2021 共享任务的最佳模型，使用 gaHealth 数据集开发的模型在翻译健康相关数据方面的 BLEU 分数最高可以提高 22.2 个百分点（40%）。此外，我们定义了开发 gaHealth 的语言指南，这是为爱尔兰语健康数据开发的第一个双语语料库，我们希望能对其他低资源数据集的创建者有所帮助。gaHealth 现在已经免费在线提供，并可用于进一步研究。

gaHealth：一个英爱双语健康数据语料库

gaHealth: An English-Irish Bilingual Corpus of Health Data

Click logs are valuable resources for a variety of information retrieval (IR)
tasks. This includes query understanding/analysis, as well as learning
effective IR models particularly when the models require large amounts of
training data. We release a large-scale domain-specific dataset of click logs,
obtained from user interactions of the Trip Database health web search engine.
Our click log dataset comprises approximately 5.2 million user interactions
collected between 2013 and 2020. We use this dataset to create a standard IR
evaluation benchmark -- TripClick -- with around 700,000 unique free-text
queries and 1.3 million pairs of query-document relevance signals, whose
relevance is estimated by two click-through models. As such, the collection is
one of the few datasets offering the necessary data richness and scale to train
neural IR models with a large amount of parameters, and notably the first in
the health domain. Using TripClick, we conduct experiments to evaluate a
variety of IR models, showing the benefits of exploiting this data to train
neural architectures. In particular, the evaluation results show that the best
performing neural IR model significantly improves the performance by a large
margin relative to classical IR models, especially for more frequent queries.

该研究发布了一份从 2013 年至 2020 年间收集的大规模特定领域点击日志数据集，用于培训神经信息检索模型。通过使用该数据集，研究人员甚至发现使用神经信息检索模型可以显着提高信息检索的性能，尤其是对于经常查询的查询。

TripClick：一家大型健康网站的日志文件

TripClick: The Log Files of a Large Health Web Search Engine

Identifying misinformation is increasingly being recognized as an important
computational task with high potential social impact. Misinformation and fake
contents are injected into almost every domain of news including politics,
health, science, business, etc., among which, the fakeness in health domain
pose serious adverse effects to scare or harm the society. Misinformation
contains scientific claims or content from social media exaggerated with strong
emotion content to attract eyeballs. In this paper, we consider the utility of
the affective character of news articles for fake news identification in the
health domain and present evidence that emotion cognizant representations are
significantly more suited for the task. We outline a technique to leverage
emotion intensity lexicons to develop emotionized text representations, and
evaluate the utility of such a representation for identifying fake news
relating to health in various supervised and unsupervised scenarios. The
consistent and significant empirical gains that we observe over a range of
technique types and parameter settings establish the utility of the emotional
information in news articles, an often overlooked aspect, for the task of
misinformation identification in the health domain.

本研究考虑了新闻文章情感信息对健康领域虚假新闻识别的作用，利用情感强度词库开发情感化文本表示技术，在多种监督和无监督情况下评估了该表示技术的实用性，结果表明情感信息的使用可以显著提高健康领域识别虚假信息的准确率。