[{"id":362241,"last_updated":"2024-01-30 16:37:08","id_people":488202,"institutes":["ILC"],"type":"journal_article","type_order":0,"type_people":"article","title":"Tell me how you write and I'll tell you what you read: a study on the writing style of book reviews","year":2023,"authors_people":"Chiara Alzetta, Felice Dell'Orletta, Alessio Miaschi, Elena Prat, Giulia Venturi","authors_cnr":["Miaschi, Alessio","Alzetta, Chiara","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Alzetta, C.","Dell'Orletta, F.","Miaschi, A.","Prat, E.","Venturi, G."],"abstract":"Purpose: The authors' goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences. Design\/methodology\/approach: The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre. Findings: The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre. Originality\/value: The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.","keywords":["Stylometric analysis","Genre detection","Natural language processing","Book reviews"],"pages":"23","url":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JD-04-2023-0073\/full\/html","volume":"79","doi":"10.1108\/JD-04-2023-0073","editors_people":"","editors":[""],"published":"Journal of documentation","publisher":"Emerald (Bingley, Regno Unito)","issn":"0022-0418","isbn":"","conference_name":"","conference_place":"","conference_date":""},{"id":362242,"last_updated":"2024-01-30 16:38:01","id_people":488203,"institutes":["ILC"],"type":"journal_article","type_order":0,"type_people":"article","title":"Testing the Effectiveness of the Diagnostic Probing Paradigm on Italian Treebanks","year":2023,"authors_people":"Alessio Miaschi, Chiara Alzetta, Dominique Brunato, Felice Dell'Orletta, Giulia Venturi","authors_cnr":["Miaschi, Alessio","Alzetta, Chiara","Dell'Orletta, Felice","Venturi, Giulia","Brunato, Dominique Pierina"],"authors_cnr_id":["14329","17692","21125"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Alzetta, C.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"The outstanding performance recently reached by neural language models (NLMs) across many natural language processing (NLP) tasks has steered the debate towards understanding whether NLMs implicitly learn linguistic competence. Probes, i.e., supervised models trained using NLM representations to predict linguistic properties, are frequently adopted to investigate this issue. However, it is still questioned if probing classification tasks really enable such investigation or if they simply hint at surface patterns in the data. This work contributes to this debate by presenting an approach to assessing the effectiveness of a suite of probing tasks aimed at testing the linguistic knowledge implicitly encoded by one of the most prominent NLMs, BERT. To this aim, we compared the performance of probes when predicting gold and automatically altered values of a set of linguistic features. Our experiments were performed on Italian and were evaluated across BERT's layers and for sentences with different lengths. As a general result, we observed higher performance in the prediction of gold values, thus suggesting that the probing model is sensitive to the distortion of feature values. However, our experiments also showed that the length of a sentence is a highly influential factor that is able to confound the probing model's predictions.","keywords":["Neural language model","Probing tasks","Treebanks"],"pages":"19","url":"https:\/\/www.mdpi.com\/2078-2489\/14\/3\/144","volume":"14","doi":"10.3390\/info14030144","editors_people":"","editors":[""],"published":"Information (Basel)","publisher":"MDPI (Basel, Svizzera)","issn":"2078-2489","isbn":"","conference_name":"","conference_place":"","conference_date":""},{"id":352735,"last_updated":"2023-11-09 18:10:01","id_people":475015,"institutes":["ILC"],"type":"journal_article","type_order":0,"type_people":"article","title":"On Robustness and Sensitivity of a Neural Language Model: A Case Study on Italian L1 Learner Errors","year":2022,"authors_people":"Miaschi, Alessio and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia","authors_cnr":["Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia","Brunato, Dominique Pierina"],"authors_cnr_id":["14329","17692","21125"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"In this paper, we propose a comprehensive linguistic study aimed at assessing the implicit behavior of one of the most prominent Neural Language Models (NLM) based on Transformer architectures, BERT (Devlin et al., 2019), when dealing with a particular source of noisy data, namely essays written by L1 Italian learners containing a variety of errors targeting grammar, orthography and lexicon. Differently from previous works, we focus on the pre-training stage and we devise two complementary evaluation tasks aimed at assessing the impact of errors on sentence-level inner representations in terms of semantic robustness and linguistic sensitivity. While the first evaluation perspective is meant to probe the model's ability to encode the semantic similarity between sentences also in the presence of errors, the second type of probing task evaluates the influence of errors on BERT's implicit knowledge of a set of raw and morpho-syntactic properties of a sentence. Our experiments show that BERT's ability to compute sentence similarity and to correctly encode multi-leveled linguistic information of a sentence are differently modulated by the category of errors and that the error hierarchies in terms of robustness and sensitivity change across layer-wise representations.","keywords":["nlp","interpretability","transformers","learner errors"],"pages":"426-438","url":"https:\/\/doi.org\/10.1109\/TASLP.2022.3226333","volume":"","doi":"10.1109\/TASLP.2022.3226333","editors_people":"","editors":[""],"published":"IEEE\/ACM transactions on audio, speech, and language processing (Online)","publisher":"[Institute of Electrical and Electronics Engineers] ([Piscataway NJ], Stati Uniti d'America)","issn":"2329-9304","isbn":"","conference_name":"","conference_place":"","conference_date":""},{"id":341004,"last_updated":"2023-11-06 19:31:21","id_people":469733,"institutes":["ILC"],"type":"journal_article","type_order":0,"type_people":"article","title":"Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties","year":2022,"authors_people":"Miaschi, Alessio and Sarti, Gabriele and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia","authors_cnr":["Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia","Brunato, Dominique Pierina"],"authors_cnr_id":["14329","17692","21125"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Sarti, G.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"In this paper, we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language. In particular, we investigate how the complexity of two different architectures of probing models affects the performance of the Transformers in encoding a wide spectrum of linguistic features. Moreover, we explore how this implicit knowledge varies according to different textual genres and language varieties.","keywords":["nlp","transformer models","interpretability"],"pages":"25-44","url":"http:\/\/www.aaccademia.it\/ita\/scheda-libro?aaref=1518","volume":"","doi":"10.4000\/ijcol.965","editors_people":"","editors":[""],"published":"Italian Journal of Computational Linguistics","publisher":"aAccademia University Press, Torino (Italia)","issn":"2499-4553","isbn":"","conference_name":"","conference_place":"","conference_date":""},{"id":341003,"last_updated":"2023-11-06 19:31:22","id_people":469732,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Punctuation Restoration in\u00a0Spoken Italian Transcripts with\u00a0Transformers","year":2022,"authors_people":"Miaschi A.; Ravelli A.A.; Dell'Orletta F.","authors_cnr":["Miaschi, Alessio","Ravelli, Andrea Amelio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Ravelli, A. A.","Dell'Orletta, F."],"abstract":"In this paper, we propose an evaluation of a Transformer-based punctuation restoration model for the Italian language. Experimenting with a BERT-base model, we perform several fine-tuning with different training data and sizes and tested them in an in- and cross-domain scenario. Moreover, we conducted an error analysis of the main weaknesses of the model related to specific punctuation marks. Finally, we test our system either quantitatively and qualitatively, by offering a typical task-oriented and a perception-based acceptability evaluation.","keywords":["nlp","transformer models","puncutation restoration"],"pages":"245-260","url":"http:\/\/www.scopus.com\/record\/display.url?eid=2-s2.0-85135083576&origin=inward","volume":"13196 LNAI","doi":"10.1007\/978-3-031-08421-8_17","editors_people":"","editors":[""],"published":"Lecture notes in computer science","publisher":"Springer (Berlin, Germania)","issn":"0302-9743","isbn":"","conference_name":"AIxIA 2021-Advances in Artificial Intelligence","conference_place":"","conference_date":"1-3\/12\/2021"},{"id":352734,"last_updated":"2023-11-06 19:31:11","id_people":474890,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Evaluating Text-To-Text Framework for Topic and Style Classification of Italian texts","year":2022,"authors_people":"Papucci, Michele; De Nigris, Chiara; Miaschi, Alessio; Dell'Orletta, Felice","authors_cnr":["Miaschi, Alessio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Papucci, M.","De Nigris, C.","Miaschi, A.","Dell'Orletta, F."],"abstract":"In this paper, we propose an extensive evaluation of the first text-to-text Italian Neural Language Model (NLM), IT5 [1], on a classification scenario. In particular, we test the performance of IT5 on several tasks involving both the classification of the topic and the style of a set of Italian posts. We assess the model in two different configurations, single- and multi-task classification, and we compare it with a more traditional NLM based on the Transformer architecture (i.e. BERT). Moreover, we test its performance in a few-shot learning scenario. We also perform a qualitative investigation on the impact of label representations in modeling the classification of the IT5 model. Results show that IT5 could achieve good results, although generally lower than the BERT model. Nevertheless, we observe a significant performance improvement of the Text-to-text model in a multi-task classification scenario. Finally, we found that altering the representation of the labels mainly impacts the classification of the topic.","keywords":["bert","style classification","t5","text-to-text","topic classification","transformers"],"pages":"56-70","url":"http:\/\/www.scopus.com\/record\/display.url?eid=2-s2.0-85143252156&origin=inward","volume":"3287","doi":"","editors_people":"","editors":[""],"published":"CEUR workshop proceedings","publisher":"M. Jeusfeld c\/o Redaktion Sun SITE, Informatik V, RWTH Aachen (Aachen, Germania)","issn":"1613-0073","isbn":"","conference_name":"Sixth Workshop on Natural Language for Artificial Intelligence, NL4AI 2022","conference_place":"","conference_date":"30\/11\/2022"},{"id":132452,"last_updated":"2023-11-06 19:31:24","id_people":454570,"institutes":["ILC"],"type":"journal_article","type_order":0,"type_people":"article","title":"A NLP-based stylometric approach for tracking the evolution of L1 written language competence","year":2021,"authors_people":"Miaschi, Alessio and Brunato, Dominique and Dell'Orletta, Felice","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Brunato, D.","Dell'Orletta, F."],"abstract":"In this study we present a Natural Language Processing (NLP)-based stylometric approach for tracking the evolution of written language competence in Italian L1 learners. The approach relies on a wide set of linguistically motivated features capturing stylistic aspects of a text, which were extracted from students' essays contained in CItA (Corpus Italiano di Apprendenti L1), the first longitudinal corpus of texts written by Italian L1 learners enrolled in the first and second year of lower secondary school. We address the problem of modeling written language development as a supervised classification task consisting in predicting the chronological order of essays written by the same student at different temporal spans. The promising results obtained in several classification scenarios allow us to conclude that it is possible to automatically model the highly relevant changes affecting written language evolution across time, as well as identifying which features are more predictive of this process. In the last part of the article, we focus the attention on the possible influence of background variables on language learning and we present preliminary results of a pilot study aiming at understanding how the observed developmental patterns are affected by information related to the school environment of the student.","keywords":["stylometry","computational linguistics","language competence"],"pages":"71-105","url":"https:\/\/www.jowr.org\/abstracts\/vol13_1\/Miaschi_et_al_2021_13_1_abstract.html","volume":"vol. 13","doi":"10.17239\/jowr-2021.13.01.03","editors_people":"","editors":[""],"published":"Journal of Writing Research","publisher":"Universiteit Antwerpen (Antwerpen, Belgio)","issn":"2030-1006","isbn":"","conference_name":"","conference_place":"","conference_date":""},{"id":341844,"last_updated":"2022-11-29 19:11:45","id_people":465394,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"On the role of textual connectives in sentence comprehension: A new dataset for Italian","year":2021,"authors_people":"Albertin G.; Miaschi A.; Brunato D.","authors_cnr":["Miaschi, Alessio","Brunato, Dominique Pierina"],"authors_cnr_id":["21125"],"authors_cnr_institute":[""],"authors":["Albertin, G.","Miaschi, A.","Brunato, D."],"abstract":"In this paper we present a new evaluation resource for Italian aimed at assessing the role of textual connectives in the comprehension of the meaning of a sentence. The resource is arranged in two sections (acceptability assessment and cloze test), each one corresponding to a distinct challenge task conceived to test how subtle modifications involving connectives in real usage sentences influence the perceived acceptability of the sentence by native speakers and Neural Language Models (NLMs). Although the main focus is the presentation of the dataset, we also provide some preliminary data comparing human judgments and NLMs performance in the two tasks.","keywords":["neural language models","textual connectives","sentence acceptability"],"pages":"","url":"http:\/\/ceur-ws.org\/Vol-3033\/paper16.pdf","volume":"3033","doi":"","editors_people":"","editors":[""],"published":"CEUR workshop proceedings","publisher":"M. Jeusfeld c\/o Redaktion Sun SITE, Informatik V, RWTH Aachen (Aachen, Germania)","issn":"1613-0073","isbn":"","conference_name":"8th Italian Conference on Computational Linguistics (CLIC-it 2021)","conference_place":"Milano","conference_date":"26-28\/01\/2022"},{"id":132487,"last_updated":"2023-11-06 19:31:30","id_people":463833,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Probing tasks under pressure","year":2021,"authors_people":"Miaschi A.; Alzetta C.; Brunato D.; Dell'Orletta F.; Venturi G.","authors_cnr":["Miaschi, Alessio","Alzetta, Chiara","Dell'Orletta, Felice","Venturi, Giulia","Brunato, Dominique Pierina"],"authors_cnr_id":["14329","17692","21125"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Alzetta, C.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"Probing tasks are frequently used to evaluate whether the representations of Neural Language Models (NLMs) encode linguistic information. However, it is still questioned if probing classification tasks really enable such investigation or they simply hint for surface patterns in the data. We present a method to investigate this question by comparing the accuracies of a set of probing tasks on gold and automatically generated control datasets. Our results suggest that probing tasks can be used as reliable diagnostic methods to investigate the linguistic information encoded in NLMs representations.","keywords":["Neural Language Models","Linguistic probing","Treebanks"],"pages":"1-7","url":"http:\/\/ceur-ws.org\/Vol-3033\/paper29.pdf","volume":"3033","doi":"","editors_people":"","editors":[""],"published":"CEUR workshop proceedings","publisher":"M. Jeusfeld c\/o Redaktion Sun SITE, Informatik V, RWTH Aachen (Aachen, Germania)","issn":"1613-0073","isbn":"","conference_name":"8th Italian Conference on Computational Linguistics (CLIC-it 2021)","conference_place":"Milano","conference_date":"29\/06-01\/07\/2022"},{"id":132451,"last_updated":"2023-11-06 19:31:35","id_people":454441,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity","year":2021,"authors_people":"Miaschi, Alessio and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"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. We first compare the sentence-level likelihood computed with BERT and the GPT-2's perplexity showing that the two metrics are correlated. In addition, we exploit linguistic features capturing a wide set of morpho-syntactic and syntactic phenomena showing how they contribute to predict the perplexity of the two NLMs.","keywords":["nlp","interpretability","deep learning"],"pages":"40-47","url":"https:\/\/www.aclweb.org\/anthology\/2021.deelio-1.5","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"978-1-954085-30-5","conference_name":"2nd Workshop on Knowledge Extraction and Integrationfor Deep Learning Architectures","conference_place":"","conference_date":"10\/06\/2021"},{"id":341002,"last_updated":"2023-11-06 19:31:26","id_people":469731,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Evaluating Transformer Models for Punctuation Restoration in Italian","year":2021,"authors_people":"Miaschi A.; Ravelli A.A.; Dell'Orletta F.","authors_cnr":["Miaschi, Alessio","Ravelli, Andrea Amelio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Ravelli, A. A.","Dell'Orletta, F."],"abstract":"In this paper, we propose an evaluation of a Transformerbased punctuation restoration model for the Italian language. Experimenting with a BERT-base model, we perform several fine-tuning with different training data and sizes and tested them in an in- and crossdomain scenario. Moreover, we offer a comparison in a multilingual setting with the same model fine-tuned on English transcriptions. Finally, we conclude with an error analysis of the main weaknesses of the model related to specific punctuation marks.","keywords":["transformer models","nlp","punctuation restoration"],"pages":"","url":"http:\/\/www.scopus.com\/record\/display.url?eid=2-s2.0-85121647978&origin=inward","volume":"3015","doi":"","editors_people":"","editors":[""],"published":"CEUR workshop proceedings","publisher":"M. Jeusfeld c\/o Redaktion Sun SITE, Informatik V, RWTH Aachen (Aachen, Germania)","issn":"1613-0073","isbn":"","conference_name":"5th Workshop on Natural Language for Artificial Intelligence (NL4AI 2021)","conference_place":"","conference_date":"29\/11\/2021"},{"id":132450,"last_updated":"2023-11-06 19:31:28","id_people":454440,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"How Do BERT Embeddings Organize Linguistic Knowledge?","year":2021,"authors_people":"Puccetti, Giovanni and Miaschi, Alessio and Dell'Orletta, Felice","authors_cnr":["Miaschi, Alessio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Puccetti, G.","Miaschi, A.","Dell'Orletta, F."],"abstract":"Several studies investigated the linguistic information implicitly encoded in Neural Language Models. Most of these works focused on quantifying the amount and type of information available within their internal representations and across their layers. In line with this scenario, we proposed a different study, based on Lasso regression, aimed at understanding how the information encoded by BERT sentence-level representations is arrange within its hidden units. Using a suite of several probing tasks, we showed the existence of a relationship between the implicit knowledge learned by the model and the number of individual units involved in the encodings of this competence. Moreover, we found that it is possible to identify groups of hidden units more relevant for specific linguistic properties.","keywords":["nlp","interpretability","deep learning"],"pages":"48-57","url":"https:\/\/www.aclweb.org\/anthology\/2021.deelio-1.6","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"978-1-954085-30-5","conference_name":"2nd Workshop on Knowledge Extraction and Integrationfor Deep Learning Architectures","conference_place":"","conference_date":"10\/06\/2021"},{"id":132418,"last_updated":"2023-11-06 19:31:53","id_people":442044,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"PRELEARN @ EVALITA 2020: Overview of the Prerequisite Relation Learning Task for Italian","year":2020,"authors_people":"Alzetta, Chiara and Miaschi, Alessio and Dell'Orletta, Felice and Koceva, Frosina and Torre, Ilaria","authors_cnr":["Miaschi, Alessio","Alzetta, Chiara","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Alzetta, C.","Miaschi, A.","Dell'Orletta, F.","Koceva, F.","Torre, I."],"abstract":"The Prerequisite Relation Learning (PRELEARN) task is the EVALITA 2020 shared task on concept prerequisite learning, which consists of classifying prerequisite relations between pairs of concepts distinguishing between prerequisite pairs and non-prerequisite pairs. Four sub-tasks were defined: two of them define different types of features that participants are allowed to use when training their model, while the other two define the classification scenarios where the proposed models would be tested. In total, 14 runs were submitted by 3 teams comprising 9 total individual participants.","keywords":["nlp","prerequisite learning","shared task"],"pages":"","url":"http:\/\/ceur-ws.org\/Vol-2765\/paper164.pdf","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"","conference_name":"Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA)","conference_place":"","conference_date":"17\/12\/2020"},{"id":132414,"last_updated":"2021-01-18 12:23:19","id_people":442042,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"ATE ABSITA@ EVALITA2020: Overview of the Aspect Term Extraction and Aspect-based Sentiment Analysis Task","year":2020,"authors_people":"De Mattei, Lorenzo and De Martino, Graziella and Iovine, Andrea and Miaschi, Alessio and Polignano, Marco and Rambelli, Giulia","authors_cnr":["Miaschi, Alessio"],"authors_cnr_id":[""],"authors_cnr_institute":[""],"authors":["De Mattei, L.","De Martino, G.","Iovine, A.","Miaschi, A.","Polignano, M.","Rambelli, G."],"abstract":"Over the last years, the rise of novel sentiment analysis techniques to assess aspect-based opinions on product reviews has become a key component for providing valuable insights to both consumers and businesses. To this extent, we propose ATE\\_ABSITA: the EVALITA 2020 shared task on Aspect Term Extraction and Aspect-Based Sentiment Analysis. In particular, we approach the task as a cascade of three subtasks: Aspect Term Extraction (ATE), Aspect-based Sentiment Analysis (ABSA) and Sentiment Analysis (SA). Therefore, we invited participants to submit systems designed to automatically identify the \"aspect terms\" in each review and to predict the sentiment expressed for each aspect, along with the sentiment of the entire review. The task received broad interest, with 27 teams registered and more than 45 participants. However, only three teams submitted their working systems. The results obtained underline the task's difficulty, but they also show how it is possible to deal with it using innovative approaches and models. Indeed, two of them are based on large pre-trained language models as typical in the current state of the art for the English language.","keywords":["nlp","sentiment analysis","shared task"],"pages":"","url":"http:\/\/ceur-ws.org\/Vol-2765\/paper153.pdf","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"","conference_name":"Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA)","conference_place":"","conference_date":"17\/12\/2020"},{"id":132416,"last_updated":"2023-11-06 19:31:44","id_people":442040,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Is Neural Language Model Perplexity Related to Readability?","year":2020,"authors_people":"Miaschi, Alessio and Alzetta, Chiara and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Alzetta, Chiara","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Alzetta, C.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"This paper explores the relationship between Neural Language Model (NLM) perplexity and sentence readability. Starting from the evidence that NLMs implicitly acquire sophisticated linguistic knowledge from a huge amount of training data, our goal is to investigate whether perplexity is affected by linguistic features used to automatically assess sentence readability and if there is a correlation between the two metrics. Our findings suggest that this correlation is actually quite weak and the two metrics are affected by different linguistic phenomena.","keywords":["nlp","neural language models","readability"],"pages":"","url":"http:\/\/ceur-ws.org\/Vol-2769\/paper_57.pdf","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"979-12-80136-28-2","conference_name":"Seventh Italian Conference on Computational Linguistics","conference_place":"","conference_date":"01-03\/03\/2021"},{"id":132391,"last_updated":"2023-11-06 19:31:46","id_people":438491,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Linguistic Profiling of a Neural Language Model","year":2020,"authors_people":"Miaschi A., Brunato D., Dell'Orletta F., Venturi G.","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"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. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.","keywords":["Linguistic Profiling","Neural Language Model","Interpretability"],"pages":"745-756","url":"https:\/\/www.aclweb.org\/anthology\/2020.coling-main.65\/","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"978-1-952148-27-9","conference_name":"International Conference on Computational Linguistics (COLING)","conference_place":"Online","conference_date":"8-13\/12\/2020"},{"id":132395,"last_updated":"2023-11-06 19:31:58","id_people":435969,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Tracking the Evolution of Written Language Competence in L2 Spanish Learners","year":2020,"authors_people":"Miaschi, Alessio; Davidson, Sam; Brunato, Dominique; Dell'Orletta, Felice; Sagae, Kenji; Sanchez-Gutierrez, Claudia H.; Venturi, Giulia","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Davidson, S.","Brunato, D.","Dell'Orletta, F.","Sagae, K.","Sanchez Gutierrez, C. H.","Venturi, G."],"abstract":"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. Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflects the explicit instruction that students receive during each course.","keywords":["Evolution of Language Competence","Natural Language Processing","Linguistic Profiling"],"pages":"92-101","url":"https:\/\/www.aclweb.org\/anthology\/2020.bea-1.9.pdf","volume":"","doi":"10.18653\/v1\/W16-05","editors_people":"","editors":[""],"published":"","publisher":"Association for Computational Linguistics (Stroudsburg, USA)","issn":"","isbn":"978-1-941643-83-9","conference_name":"15th Workshop on Innovative Use of NLP for Building Educational Applications","conference_place":"","conference_date":"10\/07\/2020"},{"id":132415,"last_updated":"2023-11-06 19:31:40","id_people":442036,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation","year":2020,"authors_people":"Miaschi, Alessio and Dell'Orletta, Felice","authors_cnr":["Miaschi, Alessio","Dell'Orletta, Felice"],"authors_cnr_id":["14329"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Dell'Orletta, F."],"abstract":"In this paper we present a comparison between the linguistic knowledge encoded in the internal representations of a contextual Language Model (BERT) and a contextual-independent one (Word2vec). We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that, although BERT is capable of understanding the full context of each word in an input sequence, the implicit knowledge encoded in its aggregated sentence representations is still comparable to that of a contextual-independent model. We also find that BERT is able to encode sentence-level properties even within single-word embeddings, obtaining comparable or even superior results than those obtained with sentence representations.","keywords":["nlp","interpretability","representation learning"],"pages":"110-119","url":"https:\/\/www.aclweb.org\/anthology\/2020.repl4nlp-1.15","volume":"","doi":"10.18653\/v1\/2020.repl4nlp-1.15","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"978-1-952148-15-6","conference_name":"5th Workshop on Representation Learning for NLP","conference_place":"","conference_date":"09\/07\/2020"},{"id":132417,"last_updated":"2023-11-06 19:31:45","id_people":442038,"institutes":["ILC"],"type":"conference_article","type_order":5,"type_people":"conferenceObject","title":"Italian Transformers Under the Linguistic Lens","year":2020,"authors_people":"Miaschi, Alessio and Sarti, Gabriele and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia","authors_cnr":["Brunato, Dominique Pierina","Miaschi, Alessio","Dell'Orletta, Felice","Venturi, Giulia"],"authors_cnr_id":["14329","17692"],"authors_cnr_institute":[""],"authors":["Miaschi, A.","Sarti, G.","Brunato, D.","Dell'Orletta, F.","Venturi, G."],"abstract":"In this paper we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language. In particular, we investigate whether and how using different architectures of probing models affects the performance of Italian transformers in encoding a wide spectrum of linguistic features. Moreover, we explore how this implicit knowledge varies according to different textual genres.","keywords":["nlp","neural language models","interpretability"],"pages":"","url":"http:\/\/ceur-ws.org\/Vol-2769\/paper_56.pdf","volume":"","doi":"","editors_people":"","editors":[""],"published":"","publisher":"","issn":"","isbn":"979-12-80136-28-2","conference_name":"Seventh Italian Conference on Computational Linguistics (CLiC-it)","conference_place":"","conference_date":"01-03\/03\/2021"}]