Papers by Martin Gleize
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction (P19-1)
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| Challenge: | Recent years have witnessed a significant increase in laboratory-based evaluation benchmarks in many scientific disciplines. |
| Approach: | They propose to use NLP datasets to extract task, dataset, metric and score from NLP papers to build automatic leaderboards. |
| Outcome: | The proposed model outperforms baselines in the NLP domain by a large margin. |
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
| Challenge: | Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations. |
| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
| Outcome: | The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio . |
HBCP Corpus: A New Resource for the Analysis of Behavioural Change Intervention Reports (2020.lrec-1)
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Francesca Bonin, Martin Gleize, Ailbhe Finnerty, Candice Moore, Charles Jochim, Emma Norris, Yufang Hou, Alison J. Wright, Debasis Ganguly, Emily Hayes, Silje Zink, Alessandra Pascale, Pol Mac Aonghusa, Susan Michie
| Challenge: | Automated extraction of the reports’ intervention content, population, settings and their results is essential in synthesising and summarising the literature. |
| Approach: | They propose to build a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation and to release an annotation dataset. |
| Outcome: | The proposed corpus and annotation dataset are being made available to the community. |
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining (P18-2)
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Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
| Challenge: | Obtaining high quality labeled data for natural language understanding tasks is slow, error-prone, complicated and expensive. |
| Approach: | They propose a method to blend weak and strong labeled data during the training of neural networks using a topic-dependent evidence detection dataset. |
| Outcome: | The proposed method improves the training of neural networks when a small amount of labeled data is available. |
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (P19-1)
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Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argument detection have made it easier to identify the more convincing arguments. |
| Approach: | They propose a new data set of pairs of evidence labeled for convincingness that is more challenging than existing alternatives. |
| Outcome: | The proposed method outperforms baselines on convincingness data and its own. |
A Summarization System for Scientific Documents (D19-3)
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Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat, Ora Peled Nakash, Odellia Boni, Haggai Roitman, Doron Cohen, Bar Weiner, Yosi Mass, Or Rivlin, Guy Lev, Achiya Jerbi, Jonathan Herzig, Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Francesca Bonin, David Konopnicki
| Challenge: | a qualitative user study identified the most valuable scenarios for scientific content consumption. |
| Approach: | They propose a system that retrieves and summarizes scientific documents for a given information need. |
| Outcome: | The proposed system ingested 270,000 scientific papers and validated with human experts. |
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics (2021.eacl-main)
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| Challenge: | Recent efforts to extract tasks, datasets and evaluation metrics from scientific literature focus on abstracts only. |
| Approach: | They propose a corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
| Outcome: | The proposed corpus contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |