LiSSS: A New Corpus of Literary Spanish Sentences for Emotions Detection

Moreno-Jiménez, Luis-Gil; Torres‐Moreno, Juan‐Manuel

doi:10.13053/cys-24-3-3474

Cited by 5 publications

(1 citation statement)

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“…More recently, sentiment analysis research has been used in a wider context of natural language understanding and reasoning [14]. New methods for multi-label emotion classification are developed [1] and new corpora for emotion detection are compiled for languages other than English [31].…”

Section: Related Workmentioning

confidence: 99%

Recognizing Emotion Cause in Conversations

et al. 2021

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We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong transformer-based baselines. The dataset is available at https:// github. com/ decla re-lab/ RECCON. Recognizing the cause behind emotions in text is a fundamental yet underexplored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause/effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset. Our transformer-based baselines, which leverage contextual pretrained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches on our dataset. We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.

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Section: Related Workmentioning

confidence: 99%