Papers by Sofie Labat

4 papers
Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing (2024.findings-emnlp)

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Challenge: Psychological studies aim at explaining internal mechanisms of emotions, while computational studies simplify them into labels.
Approach: They propose to treat emotions as strategies to cope with salient situations . they introduce a task of coping identification and a corpus constructed via role-playing .
Outcome: The proposed method allows to investigate the link between emotions and behavior, which also emerges in language.
EmoProgress: Cumulated Emotion Progression Analysis in Dreams and Customer Service Dialogues (2024.lrec-main)

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Challenge: Emotion analysis often involves categorization of isolated textual units, but these are parts of longer discourses, like dialogues or stories.
Approach: They propose a novel annotation setup for emotion categorization corpora that allows to annotate the emotion up to the annotated sentence.
Outcome: The proposed annotation setup allows to answer the question which emotion is presumably experienced at a specific moment in time.
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks (2021.naacl-main)

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Challenge: In domain-specific customer service applications, many companies struggle to deploy advanced NLP models due to the limited availability of and noise in their datasets.
Approach: They analyze customer service conversations on a multilingual social media corpus and compare different approaches to pretraining and finetuning on different end tasks.
Outcome: The proposed model improves performance on multilingual social media data, especially in non-English settings.
Identifying Cognates in English-Dutch and French-Dutch by means of Orthographic Information and Cross-lingual Word Embeddings (2020.lrec-1)

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Challenge: Existing methods to identify cognate pairs in English-Dutch and French-Dutsch combine orthographic information with cross-lingual word embeddings.
Approach: They combine traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutsch.
Outcome: The proposed classifier achieves good results on the basis of orthographic information but improves by including semantic information in the form of cross-lingual word embeddings.

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