Combining ELECTRA and Adaptive Graph Encoding for Frame Identification

This paper presents contributions in two directions: first we propose a new system for Frame Identification (FI), based on pre-trained text encoders trained discriminatively and graphs embedding, producing state of the art performance and, second, we take in consideration all the extremely different procedures used to evaluate systems for this task performing a complete evaluation over two benchmarks and all possible splits and cleaning procedures used in the FI literature

Toward a linguistically grounded dialog model for chatbot design

The increasing interest in various types of conversational interfaces has been supported by a progressive standardization of the technological frameworks used to build them. However, the landscape of available methodological frameworks for designing conversations is much more fragmented. We propose a highly generalizable methodology for designing conversational flows rooted in a functionalist-pragmatics perspective, with an explicit adherence to a conversationalist approach. In parallel, we elaborate a practical-procedural workflow for undertaking chatbots projects in which we situate the theoretical starting point. At last, we elaborate a general case- study on which we transpose the identified approach in Italian language and using one of the most authoritative NLU platforms

The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders

Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. This paper presents a computational pipeline for the automatic processing of oral and written texts: the tool enables the computation of a rich set of linguistic features at the acoustic, rhythmic, lexical, and morphosyntactic levels. Several applications of the instrument - for the detection of Mild Cognitive Impairments, Anorexia Nervosa, and Developmental Language Disorders - are also briefly discussed

UniBO @ KIPoS: Fine-tuning the Italian “BERTology" for PoS-tagging Spoken Data

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al. 2020).L’utilizzazione di word embedding contestuali ha consentito notevoli incrementi nelle performance dei sistemi automatici sviluppati per affrontare vari task nell’ambito dell’elaborazione del linguaggio naturale. Recentemente sono stati introdotti alcuni nuovi modelli sviluppati specificatamente per la lingua italiana. Lo scopo di questo lavoro è valutare se un semplice fine-tuning di questi modelli sia sufficiente per ottenere performance di alto livello nel task KIPOS di EVALITA 2020

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Overview of the EVALITA 2016 Part of speech on twitter for Italian task

The increasing interest for the extraction of various forms of knowledge from micro-blogs and social media makes crucial the development of resources and tools that can be used for automatically deal with them. PoSTWITA contributes to the advancement of the state-of-the-art for Italian language by: (a) enriching the community with a previously not existing col- lection of data extracted from Twitter and annotated with grammatical categories, to be used as a benchmark for system evaluation; (b) supporting the adaptation of Part of Speech tagging systems to this particular text domain

Parsing Italian texts together is better than parsing them alone!

In this paper we present a work aimed at testing the most advanced, state-of-the-art syntactic parsers based on deep neural networks (DNN) on Italian. We made a set of experiments by using the Universal Dependencies benchmarks and propose a new solution based on ensemble systems obtaining very good performances.In questo contributo presentia-mo alcuni esperimenti volti a verificare le prestazioni dei più avanzati parser sintattici sull’italiano utilizzando i treebank disponibili nell’ambito delle Universal Dependencies. Proponiamo inoltre un nuovo sistema basato sull’ ensemble par-sing che ha mostrato ottime prestazioni

Using Deep Neural Networks for Smoothing Pitch Profiles in Connected Speech

This paper presents a new pitch tracking smoother based on deep neural networks (DNN). It leverages Long Short-Term Memories, a particular kind of recurrent neural network, for correcting pitch detection errors produced by state-of-the-art Pitch Detection Algorithms. The proposed system has been extensively tested using two reference benchmarks for English and exhibited very good performances in correcting pitch detection algorithms outputs when compared with the gold standard obtained with laryngographs

State-of-the-art Italian dependency parsers based on neural and ensemble systems

In this paper we present a work which aims to test the most advanced, state-of-the-art syntactic dependency parsers based on deep neural networks (DNN) on Italian. We made a large set of experiments by using two Italian treebanks containing different text types downloaded from the Universal Dependencies project and propose a new solution based on ensemble systems. We implemented the proposed ensemble solutions by testing different techniques described in literature, obtaining very good parsing results, well above the state of the art for Italian