Papers by Giulia Venturi
Profiling-UD: a Tool for Linguistic Profiling of Texts (2020.lrec-1)
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| Challenge: | Profiling–UD is a text analysis tool that can be used to characterize language variation from different perspectives. |
| Approach: | They introduce Profiling–UD, a text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives. |
| Outcome: | The proposed tool is specifically designed to be multilingual since it is based on the Universal Dependencies framework. |
Evaluating Large Language Models via Linguistic Profiling (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks. |
| Approach: | They propose a new evaluation methodology to test LLMs' sentence generation abilities under specific linguistic constraints. |
| Outcome: | The proposed evaluation methodology is based on the 'linguistic profiling' approach and is not intended to be a task-oriented evaluation. |
On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence (2022.coling-1)
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| Challenge: | Several studies on the interpretation of Neural Language Models (NLMs) focus on the linguistic generalization abilities of pre-trained models, but little attention is paid to how the linguistic knowledge of the models changes during fine-tuning. |
| Approach: | They propose to examine whether a wide range of linguistic phenomena are forgotten during fine-tuning and whether it is possible to predict the fine- tuned accuracy solely relying on the assessed linguistic competence. |
| Outcome: | The proposed model can predict the evolution of written language competence of native language learners based on the assessed linguistic competence. |
“Voices of the Great War”: A Richly Annotated Corpus of Italian Texts on the First World War (2020.lrec-1)
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Federico Boschetti, Irene De Felice, Stefano Dei Rossi, Felice Dell’Orletta, Michele Di Giorgio, Martina Miliani, Lucia C. Passaro, Angelica Puddu, Giulia Venturi, Nicola Labanca, Alessandro Lenci, Simonetta Montemagni
| Challenge: | “Voices of the Great War” is the first large corpus of Italian historical texts dating back to the period of First World War. |
| Approach: | "Voices of the Great War" is the first large corpus of Italian historical texts dating back to the period of First World War. |
| Outcome: | The "Voices of the Great War" corpus is the first large corpus of Italian historical texts dating back to the period of First World War. |
Universal Dependencies and Quantitative Typological Trends. A Case Study on Word Order (L18-1)
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| Challenge: | a new method is proposed to acquire typological evidence from "gold" treebanks for different languages. |
| Approach: | They propose a method for acquiring typological evidence from "gold" treebanks for different languages. |
| Outcome: | The proposed method can shed light on key issues of the linguistic typological literature. |
Is this Sentence Difficult? Do you Agree? (D18-1)
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| Challenge: | a crowdsourcing-based approach to model sentence complexity is proposed . word-level predictors shown to correlate with greater processing difficulties are e.g. word frequency, age of acquisition, root frequency effect, orthographic neighbourhood frequency . |
| Approach: | They propose a crowdsourcing-based approach to model human perception of sentence complexity using a corpus of sentences rated with judgments of complexity for two typologically-different languages. |
| Outcome: | The proposed model predicts agreement among annotators independently from the assigned judgment and the perception of sentence complexity in Italian and English. |
Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It) (2024.lrec-main)
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| Challenge: | a recent study has shown that pre-trained NLMs can capture syntax- and semantic-sensitive phenomena. |
| Approach: | They investigate whether fine-tuning pre-trained models with linguistic knowledge improves their performance in a target task. |
| Outcome: | The proposed enhancements improve models' performance in a target task, the authors show . the study includes models in Italian and English, and multilingual models in English and Italian . |
Linguistic Profiling of a Neural Language Model (2020.coling-main)
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| Challenge: | Neural Language Models (NLMs) have become a central component in NLP systems over the last few years, showing outstanding performance and improving the state-of-the-art on many tasks. |
| Approach: | They use a wide set of probing tasks to investigate how a Neural Language Model learns linguistic properties before and after a fine-tuning process. |
| Outcome: | The proposed model can encode a wide range of linguistic characteristics but loses this information when trained on specific downstream tasks. |