Papers by Jose G. Moreno
Understanding Feature Focus in Multitask Settings for Lexico-semantic Relation Identification (2021.findings-acl)
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| Challenge: | Lexico-semantic relations embody symmetric and asymmetric linguistic phenomena such as synonymy (e.g. phone telephone), cohyponymy (, cohypoonymy, hypernymy, meronymy) and more can be enumerated. |
| Approach: | They propose to combine feature engineering and multitask architectures to identify lexico-semantic relations by combining asymmetric distributional features with shared-private models. |
| Outcome: | The proposed models improve over binary and fully-shared classifiers and balance the focus on features between private and shared layers 1 and 2 . |
Rebalancing Label Distribution While Eliminating Inherent Waiting Time in Multi Label Active Learning Applied to Transformers (2024.lrec-main)
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| Challenge: | Data annotation is a resourceintensive endeavor, necessitating human involvement and expertise. |
| Approach: | They propose to annotate instances to rebalance label distribution by judiciously selecting and limiting the data to be annotated. |
| Outcome: | The proposed method mitigates biases, improves model performance and reduces strategy-dependent disparities. |
Limitations of Human Identification of Automatically Generated Text (2024.lrec-main)
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Nadège Alavoine, Maximin Coavoux, Emmanuelle Esperança-Rodier, Romane Gallienne, Carlos Gonzalez Gallardo, Jérôme Goulian, Jose G. Moreno, Aurélie Névéol, Didier Schwab, Vincent Segonne, Johanna Simoens
| Challenge: | Neural text generation tools such as ChatGPT are gaining popularity . human annotations are considered gold standard labels for multiple tasks . |
| Approach: | They propose a new corpus in French and English for recognising automatically generated texts . they propose 'incontext' setup which makes explicit the interaction between two parties . |
| Outcome: | The proposed model generates fluent text, which requires much closer reading than the current model. |
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop? (2022.coling-1)
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| Challenge: | Recent developments have shown that pre-trained language models are effective soft reasoners over language. |
| Approach: | They propose to model multi-hop reasoning process as a sequence of explicit single-hop steps. |
| Outcome: | The proposed model improves on multiple-choice question answering and reading comprehension with 68.4% and 16.0% w.r.t. classic PLMs. |
Knowledge Base Embedding By Cooperative Knowledge Distillation (2020.coling-main)
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| Challenge: | Knowledge bases are increasingly exploited as gold standard data sources for various knowledge-driven NLP tasks. |
| Approach: | They propose a method to perform knowledge base representation learning by mutually and jointly distilling knowledge within a dynamic teacher-student setting. |
| Outcome: | The proposed approach outperforms two baselines, traditional and sequential, on two standard datasets showing that it is possible to distill knowledge between KBs. |