From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation (2020.coling-main)
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| Challenge: | Existing sentiment analysis models lack temporal information to capture semantics of long texts. |
| Approach: | They propose a framework to exploit task-related discourse structures for sentiment analysis. |
| Outcome: | The proposed framework improves the performance even beyond existing approaches based on human annotated data. |
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| Challenge: | Existing discourse treebanks are limited in the application of data-driven approaches to discourse parsing. |
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| Challenge: | Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization. |
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
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| Challenge: | Existing studies tend to extract the sentiment elements in a generative manner to avoid complex modeling of sentiment elements. |
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Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)
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Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
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