Teachers and students are increasingly relying on online learning resources
to supplement the ones provided in school. This increase in the breadth and
depth of available resources is a great thing for students, but only provided
they are able to find answers to their queries. Question-answering and
information retrieval systems have benefited from public datasets to train and
evaluate their algorithms, but most of these datasets have been in English text
written by and for adults. We introduce a new public French question-answering
dataset collected from Alloprof, a Quebec-based primary and high-school help
website, containing 29 349 questions and their explanations in a variety of
school subjects from 10 368 students, with more than half of the explanations
containing links to other questions or some of the 2 596 reference pages on the
website. We also present a case study of this dataset in an information
retrieval task. This dataset was collected on the Alloprof public forum, with
all questions verified for their appropriateness and the explanations verified
both for their appropriateness and their relevance to the question. To predict
relevant documents, architectures using pre-trained BERT models were fine-tuned
and evaluated. This dataset will allow researchers to develop
question-answering, information retrieval and other algorithms specifically for
the French speaking education context. Furthermore, the range of language
proficiency, images, mathematical symbols and spelling mistakes will
necessitate algorithms based on a multimodal comprehension. The case study we
present as a baseline shows an approach that relies on recent techniques
provides an acceptable performance level, but more work is necessary before it
can reliably be used and trusted in a production setting.

介绍了一个从 Alloprof 网站收集的法语问题回答数据集，其中包含 29,349 个问题及其解释，以及一个信息检索任务的案例研究，用预训练的 BERT 模型 fine-tuned 和评估，这个数据集将为研究人员开发特定于法语教育背景的算法提供帮助。

Alloprof: 一个新的法语问答教育数据集及其在信息检索案例研究中的应用

Alloprof: a new French question-answer education dataset and its use in an information retrieval case study

Emotion analysis in texts suffers from two major limitations: annotated
gold-standard corpora are mostly small and homogeneous, and emotion
identification is often simplified as a sentence-level classification problem.
To address these issues, we introduce a new annotation scheme for exploring
emotions and their causes, along with a new French dataset composed of
autobiographical accounts of an emotional scene. The texts were collected by
applying the Cognitive Analysis of Emotions developed by A. Finkel to help
people improve on their emotion management. The method requires the manual
analysis of an emotional event by a coach trained in Cognitive Analysis. We
present a rule-based approach to automatically annotate emotions and their
semantic roles (e.g. emotion causes) to facilitate the identification of
relevant aspects by the coach. We investigate future directions for emotion
analysis using graph structures.

本研究介绍一种新的情感分析标注方法，提出一种基于自传故事纪录的情感分析的数据集，探索了使用基于规则的自动标注方法来标注情感及其语义角色，以便教练识别相关方面，并探讨了基于图结构的情感分析的未来方向。