Papers by Lorenzo Lupo

2 papers
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models (2022.acl-long)

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Challenge: Multi-encoder models aim to improve translation quality by encoding document-level contextual information alongside the current sentence.
Approach: They propose to pre-train contextual parameters over split sentence pairs to improve contextual encoding . they propose four different splitting methods to improve learning of contextual parameters .
Outcome: The proposed model improves learning of contextual parameters, both in low and high resource settings.
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods (2024.lrec-main)

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Challenge: Social scientists increasingly use demographically stratified social media data to study attitudes, beliefs, and behavior of the general public.
Approach: They validated the DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures.
Outcome: The best XLM-based classifier improves upon the commonly used competitor M3 by up to 53% F1.

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