Papers by Sven Buechel
Modeling Empathy and Distress in Reaction to News Stories (D18-1)
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| Challenge: | a recent work on empathy prediction has underestimated the complexity of the phenomenon and lacks a shared corpus. authors present a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. |
| Approach: | They propose a method which captures empathy assessments by the writer of a statement using multi-item scales. |
| Outcome: | The proposed method distinguishes between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. |
Towards Label-Agnostic Emotion Embeddings (2021.emnlp-main)
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| Challenge: | Existing representation schemes for emotion analysis are based on label formats, natural languages, and even disparate model architectures. |
| Approach: | They propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. |
| Outcome: | The proposed model performs well on a wide range of datasets without penalizing prediction quality. |
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena (C18-2)
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| Challenge: | a new version of a dictionary for the humanities provides time-variant information on word meanings and lexical emotions. |
| Approach: | They propose an extended version of JeSemE for visualizing computationally derived time-variant information on word meanings and lexical emotions. |
| Outcome: | The proposed tool combines state-of-the-art distributional semantics with a nuanced model of human emotions. |
A Time Series Analysis of Emotional Loading in Central Bank Statements (D19-51)
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| Challenge: | a recent study has found that central bankers are communicating proactively to economic agents, resulting in a rapid growth of economic literature. |
| Approach: | They examine the affective content of central bank press statements using emotion analysis . they focus on the European Central Bank and the US Federal Reserve Bank . |
| Outcome: | The results show that the ECB and the Fed have strong emotional dimensions . the authors suggest that the use of emotion analysis could reveal latent emotions . |
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons (L18-1)
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| Challenge: | Existing representational frameworks for emotion encoding are incompatible with semantic polarity, resulting in a large amount of incompatible emotion lexicons. |
| Approach: | They propose to map different emotion representation formats onto each other for mutual compatibility and interoperability of language resources. |
| Outcome: | The proposed method produces (near-)gold quality emotion lexicons even in crosslingual settings. |
Sharing Copies of Synthetic Clinical Corpora without Physical Distribution — A Case Study to Get Around IPRs and Privacy Constraints Featuring the German JSYNCC Corpus (L18-1)
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| Challenge: | eu legal culture imposes unsurmountable hurdles to exploit copyright protected language data . legal constraints have seriously hampered progress in resource-greedy NLP research . authors propose a new approach for the creation and re-use of clinical corpora . |
| Approach: | They propose a method for the creation and re-use of clinical corpora based on a two-step workflow . they substitute authentic clinical documents by synthetic ones, i.e., made-up reports and case studies . |
| Outcome: | a new approach replaces authentic clinical documents by synthetic ones, i.e., made-up reports and case studies published in medical e-textbooks. |
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (N18-1)
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| Challenge: | a recent shift towards expressive emotion representation models has hampered deep learning in sentiment analysis. |
| Approach: | They propose a multi-task learning problem to solve a language data bottleneck . they propose to use word emotion induction as an individual task to predict emotion . |
| Outcome: | The proposed model outperforms a wide range of other methods on 9 languages and 15 conditions. |
Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level (C18-1)
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| Challenge: | Emotion Representation Mapping (ERM) is an alternative to Word Emotion Induction (WEI) for automatic emotion lexicon construction. |
| Approach: | They propose a neural network approach to ERM that converts existing emotion ratings from one representation format into another by mapping Valence-Arousal-Dominance annotations into Ekman’s Basic Emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art in 13 languages and is almost as reliable as human annotations even in cross-lingual settings. |
Learning Word Ratings for Empathy and Distress from Document-Level User Responses (2020.lrec-1)
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| Challenge: | Emotion analysis of text is increasing in popularity in NLP, however, manually creating lexica for psychological constructs such as empathy has proven difficult. |
| Approach: | They compare different approaches to learning word ratings from higher-level supervision and use a Mixed-Level Feed Forward Network to create the first-ever empathy lexicon. |
| Outcome: | The proposed model automatically creates empathy word ratings from document-level ratings. |
Learning and Evaluating Emotion Lexicons for 91 Languages (2020.acl-main)
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| Challenge: | Emotion lexicons describe the affective meaning of words but are limited in coverage for most languages. |
| Approach: | They propose a method for creating arbitrarily large emotion lexicons for any target language. |
| Outcome: | The proposed method exceeds human reliability for some languages and variables. |