Papers with manual classification
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold (2022.findings-acl)
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| Challenge: | a prototypical NLP experiment trains a standard architecture on labeled English data . a recent study shows that research often goes beyond the square one setup . |
| Approach: | They argue that the prototypical NLP experiment trains a standard architecture on labeled English data and optimizes for accuracy without accounting for other dimensions such as fairness, interpretability, or computational efficiency. |
| Outcome: | The proposed model steers and biases the research dynamics in the NLP community, the authors argue . they show that the prototype biased recent NLP research on English data is true . |
Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition (2024.lrec-main)
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| Challenge: | Using an emotion-annotated dataset, we aim to provide a resource for the scientific community to study the emotional intricacies of classical literature. |
| Approach: | They propose to provide an emotion-annotated dataset for classical literature and Western mythology using a multivariate time series and a deep learning masked language model. |
| Outcome: | The proposed dataset reveals compelling patterns and phenomena within the Iliad's emotional landscape. |
An Empirical Comparison of Question Classification Methods for Question Answering Systems (2020.lrec-1)
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| Challenge: | Existing methods for Question Classification are monolingual, but they are not suitable for low-resourced languages. |
| Approach: | They propose to classify the most recent methods in four different categories . they propose to use a low, medium, high, and very high level of dependency on external resources . |
| Outcome: | The proposed method outperforms methods not suitable for low-resource languages. |