Papers by Francesco Molfese
Neuralign: A Context-Aware, Cross-Lingual and Fully-Neural Sentence Alignment System for Long Texts (2024.eacl-long)
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| Challenge: | Existing sentence alignment systems focus on auxiliary information such as document metadata and hyperparameter-sensitive techniques, and neglect the crucial role that context plays in the alignment process. |
| Approach: | They propose a context-aware, end-to-end and fully-neural architecture for sentence alignment that maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
| Outcome: | The proposed system maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering (2024.emnlp-main)
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| Challenge: | Current Large Language Models (LLMs) have shown strong reasoning capabilities in commonsense question answering benchmarks, but the process underlying their success remains largely opaque. |
| Approach: | They propose a zero-shot question answering framework that combines retrieval, case-based reasoning and introspection to improve the model's performance and interpretability. |
| Outcome: | The proposed framework outperforms existing LLMs and previous knowledge integration approaches in commonsense reasoning benchmarks and achieves an average accuracy improvement of 4.5 points. |