Papers by Alexander Gelbukh
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020.findings-emnlp)
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| Challenge: | Current methods for emotion recognition in conversations often face difficulties in context propagation, emotion shift detection, and differentiating between related emotion classes. |
| Approach: | They propose a framework that incorporates mental states, events, and causal relations to learn interactions between interlocutors participating in a conversation. |
| Outcome: | The proposed framework improves on four conversational benchmark datasets. |
MIME: MIMicking Emotions for Empathetic Response Generation (2020.emnlp-main)
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Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
| Challenge: | Empathy is a fundamental human trait that reflects our ability to understand and reflect the thoughts and feelings of the people we interact with. |
| Approach: | They propose to use polarity-based emotion clusters to generate empathetic responses . they also introduce stochasticity into the emotion mixture that yields emotionally more varied responses compared to the previous work . |
| Outcome: | The proposed methods improve empathy and contextual relevance of the response, and introduce stochasticity into the emotion mixture that yields emotionally more varied responses than the previous work. |
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)
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| Challenge: | Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback. |
| Approach: | They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains. |
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (D19-1)
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| Challenge: | Emotion recognition in conversation (ERC) has received much attention lately due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. |
| Approach: | They propose a graph neural network-based approach to emotion recognition in conversation that leverages self and inter-speaker dependency of the interlocutors to model conversational context. |
| Outcome: | The proposed method outperforms the current state-of-the-art on a number of benchmark emotion classification datasets while minimizing context propagation issues. |
NLP Progress in Indigenous Latin American Languages (2024.naacl-long)
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Atnafu Tonja, Fazlourrahman Balouchzahi, Sabur Butt, Olga Kolesnikova, Hector Ceballos, Alexander Gelbukh, Thamar Solorio
| Challenge: | a new study examines the marginalization of indigenous languages in the face of rapid technological advancements. |
| Approach: | They highlight the cultural richness of indigenous languages and the risk they face of being overlooked in the realm of natural language processing. |
| Outcome: | The authors highlight the cultural richness of indigenous languages and their risk of being overlooked in the realm of natural language processing. |