Search Results for author:
Found 5 papers, 5 papers with code
DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods
DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users.
How to Use Large Language Models for Text Coding: The Case of Fatherhood Roles in Public Policy Documents
Recent advances in large language models (LLMs) like GPT-3 and GPT-4 have opened up new opportunities for text analysis in political science.
Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenation
Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture.
Focused Concatenation for Context-Aware Neural Machine Translation
A straightforward approach to context-aware neural machine translation consists in feeding the standard encoder-decoder architecture with a window of consecutive sentences, formed by the current sentence and a number of sentences from its context concatenated to it.
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models
Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence.
