Papers by Sofie Labat
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. |