27 citations · 128 across the 13 of their papers we have counts for
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The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks
Brihi Joshi, Neil Shah, Francesco Barbieri +1
Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis…
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves +1
The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sent…
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning
Xisen Jin, Francesco Barbieri, Brendan Kennedy +3
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution. Previous w…