Search Results for author:
Found 24 papers, 1 papers with code
On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence
Several studies in the literature on the interpretation of Neural Language Models (NLM) focus on the linguistic generalization abilities of pre-trained models.
Making Italian Parliamentary Records Machine-Actionable: the Construction of the ParlaMint-IT corpus
The corpus contains 1199 sessions and 79, 373 speeches, for a total of about 31 million words and was encoded according to the ParlaCLARIN TEI XML format, as well as in CoNLL-UD format.
SemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge
We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation.
What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity
This paper presents an investigation aimed at studying how the linguistic structure of a sentence affects the perplexity of two of the most popular Neural Language Models (NLMs), BERT and GPT-2.
Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It)
In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task.
Linguistic Profiling of a Neural Language Model
In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems.
Tracking the Evolution of Written Language Competence in L2 Spanish Learners
In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students{'} written productions.
Profiling-UD: a Tool for Linguistic Profiling of Texts
In this paper, we introduce Profiling{--}UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives.
``Voices of the Great War'': A Richly Annotated Corpus of Italian Texts on the First World War
{``}Voices of the Great War{''} is the first large corpus of Italian historical texts dating back to the period of First World War.
Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the Italian Universal Dependency Treebank
For both evaluation datasets, the performance of parsers increases, in terms of the standard LAS and UAS measures and of a more focused measure taking into account only relations involved in error patterns, and at the level of individual dependencies.
Is this Sentence Difficult? Do you Agree?
In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity.
CItA: an L1 Italian Learners Corpus to Study the Development of Writing Competence
In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school.
T2K\textasciicircum2: a System for Automatically Extracting and Organizing Knowledge from Texts
In this paper, we present T2K{\textasciicircum}2, a suite of tools for automatically extracting domain―specific knowledge from collections of Italian and English texts.
Enriching the ISST-TANL Corpus with Semantic Frames
The paper describes the design and the results of a manual annotation methodology devoted to enrich the ISST--TANL Corpus, derived from the Italian Syntactic--Semantic Treebank (ISST), with Semantic Frames information.
