The objective of this paper is to predict (A)~whether a sentence in a written text expresses an emotion, (B) the mode(s) in which the emotion is expressed, (C) whether it is basic or complex, and (D) its emotional category.
One of our major contributions, in addition to a dataset and a model, is to integrate the fact that an emotion can be expressed in different modes: from a direct mode, essentially lexicalized, to a more indirect mode, where emotions will only be suggested, a mode that NLP approaches generally don't take into account. 

The scope is on written texts, i.e. it does not focus on conversational or multi-modal data. In this context, modes of expression are seen as a factor towards the automatic analysis of complexity in texts.

Experiments on French texts show acceptable results compared to the human annotators' agreement to predict the mode and category, and outperforming results compared to using a large language model with in-context learning (i.e. no fine-tuning) on all tasks.

Dataset and model can be downloaded on HuggingFace: https://huggingface.co/TextToKids .

Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

This paper focuses on the generation of natural language questions based on SPARQL queries, with an emphasis on conversational use cases (follow-up question-answering). It studies what can be achieved so far based on current deep learning models (namely pretrained T5 and BART models). To do so, 4 knowledge-based QA corpora have been homogenized for the task and a new challenge set is introduced. A first series of experiments analyzes the impact of different training setups, while a second series seeks to understand what is still difficult for these models. The results from automatic metrics and human evaluation show that simple questions and frequent templates of SPARQL queries are usually well processed whereas complex questions and conversational dimensions (coreferences and ellipses) are still difficult to handle. The experimental material is publicly available on https://github.com/Orange-OpenSource/sparql-to-text .

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.11/)

SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications

*Coffee Breaks will be provided between 10:30 and 11:00 and 15:30 and 16:00.* To view the workshop program in detail, click [here](https://workshop-wassa.github.io/2024/schedule/) [![](https://assets.underline.io/markdown_image/1/image/12613369ab2f6dd75393ffa8794ccdd3.png)](https://app.gather.town/app/7Rf2IGR6LODvPyOx/ACL2024Workshops?spawnToken=bXLFSgsfQRy_5gc4ywWb)

W15: The 14th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA)

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The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

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Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications

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