Papers by Marc Franco-Salvador
What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts (2021.acl-short)
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| Challenge: | According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people’s everyday life and career. |
| Approach: | They propose to model a task that automatically detects three basic needs on short posts in English and then apply them to a binary task. |
| Outcome: | The proposed model achieves similar performance as a trained human annotator in the real-world. |
Few-Shot Learning with Siamese Networks and Label Tuning (2022.acl-long)
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| Challenge: | Recent studies have shown that few-shot text classification is a poor solution for training data-intensive tasks. |
| Approach: | They propose a method that embeds texts and labels into classifiers with proper pre-training. |
| Outcome: | The proposed approach reduces inference cost by increasing the number of labels and embeddings. |
CATS: A Tool for Customized Alignment of Text Simplification Corpora (L18-1)
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| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning (2020.lrec-1)
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| Challenge: | Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
| Approach: | They propose an online learning-based aspect extraction solution that allows users to post-edit the aspect extraction with little effort. |
| Outcome: | The proposed solution dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model. |
PyRater: A Python Toolkit for Annotation Analysis (2024.lrec-main)
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| Challenge: | PyRater is an open-source Python toolkit for analysing corpora annotations. |
| Approach: | They propose to use PyRater to analyse corpora annotations. |
| Outcome: | The proposed model can be used to identify the best annotations and retrieve the gold standard. |
Zero-Shot Data Maps. Efficient Dataset Cartography Without Model Training (2023.findings-emnlp)
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| Challenge: | Existing methods to diagnose large annotated datasets require the fitting of a strong model to the dataset. |
| Approach: | They propose a new approach to compute confidence and variability over an ensemble of zero-shot models constructed with different but semantically equivalent label descriptions. |
| Outcome: | The proposed method can be used to diagnose large annotated datasets with accuracy up to 14x faster than the current method. |