Papers by Fabio Rinaldi
Early Guessing for Dialect Identification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Current research on dialect identification is model-centric, focusing on performance. |
| Approach: | They propose a data-centric approach to find the shortest input needed to make a plausible guess. |
| Outcome: | The proposed method generalizes across dialects and datasets with two shortening criteria. |
Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. (2021.naacl-srw)
Copied to clipboard
| Challenge: | Negation resolution remains an acute and continuously researched question in Natural Language Processing. |
| Approach: | They propose to use multilingual pre-trained general representation models to detect negation scope in languages without annotated data. |
| Outcome: | The proposed model achieves token-level F1 score between English, Spanish, French, and Russian. |
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) pre-trained on massive text data in many languages are preferred solution for various Natural Language processing tasks. |
| Approach: | They compare tokenization parity and information parity as representational biases in pre-trained models . they find TP is better predictor of performance on tasks reliant on syntactic and morphological cues . |
| Outcome: | The proposed model improves on dialect classification, topic classification, and extractive question answering tasks. |
UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition (D19-57)
Copied to clipboard
| Challenge: | CRAFT shared task 2019: concept recognition using named entity recognition and normalization . a biLSTM-based network and a transformer system were used to tackle both tasks in a single model . |
| Approach: | They propose two different neural approaches to concept recognition . they propose a BiLSTM-based network and a bioBERT-based system for NER and normalization . |
| Outcome: | The proposed systems model the task as a sequence labeling problem. |
BUST: Benchmark for the evaluation of detectors of LLM-Generated Text (2024.naacl-long)
Copied to clipboard
| Challenge: | Using the benchmark, we evaluated 5 detectors and found substantial performance variance across tasks. |
| Approach: | They propose to evaluate detectors of texts generated by instruction-tuned large language models (LLMs) using a benchmark dataset, they evaluated 5 detectors and found substantial performance variance across tasks. |
| Outcome: | The proposed benchmarks evaluated 5 detectors and found substantial performance variance across tasks. |