alemiaschi/clic-it_corpus huggingface dataset:
Progettare chatbot: considerazioni e linee guida
Authors: Eleonora Mollo, Amon Rapp, Dario Mana, Rossana Simeoni (Università di Torino, 2018)
Topic: Chatbots and Dialogue Systems

Abstract
This work is focused on the curEnglish. This work is focused on the current trends in designing chatbots and virtual assistants. We start from users’ needs identified in industrial surveys on chatbots. The result is a collection of guidelines and considerations which reflect the state of the art.

Introduction
Chatbots and virtual assistants are a developing field. Numerous companies are moving to synchronize their marketing, sales, and support functions in order to offer their users a positive experience that meets their expectations during interaction. According to research conducted by Oracle, "Can Virtual Experience Replace Reality" (Oracle, 2016), B2B and B2C brands have understood that there is significant room for improvement in their activities through the support of artificial intelligence: one of their top priorities is certainly the enhancement of Customer Experience (CX). 78% of the brands surveyed have implemented or planned to make investments in Artificial Intelligence (AI) or Virtual Reality by 2020. With the aim of improving CX, this research aims to analyze current strategies for building chatbots and virtual assistants. Below, the needs of users regarding interaction with chatbots are outlined through a survey review conducted by major industry players, such as Capgemini (Capgemini, 2017) and Amdocs (Amdocs, 2017). Subsequently, a survey was conducted to understand which features virtual assistants should have, outlining emerging trends regarding "best practices" in design. The result is a set of guidelines that a designer should follow when intending to build a chatbot capable of meeting CX expectations. With the objective of a preliminary exploration of the field rather than a systematic review, the research was conducted using Google Scholar in early 2018 with the following keywords: conversational interface, invisible UI, no UI, digital assistant, chatbot, chatops, scrollytelling, conversational design patterns, question answering.

Conclusion
Thanks to the analysis and research work carried out, it has been possible to identify, at least at a preliminary level, the guidelines that can be used in the design phase of chatbots, specifying which of these guidelines are still under discussion and acceptance, and which have become established practices for chatbot design. These guidelines are currently being discussed within the framework of the TIM project "Cognitive Solution for Intelligent Caring" (TIM Technical Bulletin, 2018) with the aim of their adoption to ensure an effective CX.
Long-term Social Media Data Collection at the University of Turin
Authors: Valerio Basile, Mirko Lai, Manuela Sanguinetti (Università di Torino, 2018)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
We report on the collection of social media messages — from Twitter in particular — in the Italian language that is continuously going on since 2012 at the University of Turin. A number of smaller datasets have been extracted from the main collection and enriched with different kinds of annotations for linguistic purposes. Moreover, a few extra datasets have been collected independently and are now in the process of being merged with the main collection. We aim at making the resource available to the community to the best of our possibility, in accordance with the Terms of Service provided by the platforms where data have been gathered from.

Introduction
The online micro-blogging platform Twitter1 has been a popular source for natural language data since the second half of the 2010’s, due to the enormous quantity of public messages exchanged 1https://twitter.com/ by its users, and the relative ease of collecting them through the ofﬁcial API. Many researchers implemented systems to collect large datasets of tweets, and share them with the community. Among them, the Contentcentered Computing group at the University of Turin2 is maintaining a large, diversiﬁed collection of datasets of tweets in the Italian language3. However, although the Twitter datasets in Italian make the majority of our collection, over the years, and also in the recent past, several resources have been created in other languages and including data retrieved from other sources than Twitter. In this paper, we report on the current status of the collection (Section 2) and we give an overview of several annotated datasets included in it (Section 3). Finally, we describe our current and future plans to make the data and annotations available to the research community (Section 4).

Conclusion
PESInet: Automatic Recognition of Italian Statements, Questions, and Exclamations With Neural Networks
Authors: Sonia Cenceschi, Roberto Tedesco, Licia Sbattella, Davide Losio, Mauro Luchetti (SUPSI University, Politecnico di Milano, 2019)
Topic: Multimodal

Abstract
PESInet is an Automatic Prosody Recognition system aiming at classifying Information Units as Statement, Question, or Exclamation. PESInet adopts a modular architecture, with a master NN evaluating the results of two independent BLSTM NNs that work on audio and its transcription. PESInet has been trained with our own three-class, balanced corpus composed of about 1.5 million text phrases and 60,000 utterances of recited and spontaneous speech. PESInet reached an accuracy of 80% on three classes, and 91% on two classes (Question vs Non-question). Finally, PESInet, compared against human listeners on a two-class test based on a different corpus, reached a better accuracy (89% for PESInet, against 80% for human listeners).

Introduction
The goal of PESInet was to investigate whether clues derived from text could improve the recognition of simple prosodic forms in Information Units (IUs). In particular, we focused on Statement, Question, and Exclamation, which are proposition’s structures and are independent of the pragmatic function of the corresponding IU: each one can assume a large set of illocutionary acts, as explained in the Language into Act theory (L-AcT) described in Cresti (2014). An IU is composed of a textual realisation (i.e., a written phrase) and an acoustic realisation (an audio recording of a speaker uttering such a phrase), and conveys a specific informative intention (Austin, 1975; Cresti, 2000). We designed a modular model based on Neural Networks (NNs), able to highlight how much audio and text affected recognition accuracy. Moreover, to validate our results, we compared our NN model against human listeners, on a set of IUs that did not overlap with the corpus we used to train the model.

Conclusion
PESInet got an accuracy of 80% on three classes and 91% on two classes. Moreover, PESInet reached very good results when compared to human listeners on a totally different corpus. Although this human/NN comparison should be taken with a grain of salt, we believe that it is a hint that the network works well and the results are truly promising. As future work, more recordings should be added to ACorpus and MCorpus to improve the performance of the Audio-based NN and, as a consequence, of the whole PESInet. Currently, we are working on cleaning the code and streamlining the training procedure, as we plan to release the code.
Robospierre, an Artificial Intelligence to Solve “La Ghigliottina”
Authors: Nicola Cirillo, Chiara Pericolo, Pasquale Tufano (Università di Salerno, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper describes Robospierre, a system developed to solve the language game “La Ghigliottina” (the guillotine). To find the solution of a game instance, it relies on MWEs (Multiword Expressions) automatically extracted through a lexicalized association rules algorithm; on a list of proverbs; and on some lists of titles.

Introduction
“La Ghigliottina” is the final game of “L’Eredità”, an Italian quiz show. In this game, the player should find a word linked to a set of five clue words. For example, if these words are table, works, watch, Premier League and police, the player should give as solution the word calendar. The link between a clue and the solution is usually the fact that both these words are part of an MWE (Multi-Word Expression), e.g. table and calendar are linked because they are part of the MWE *table calendar*. However, there can be also other kinds of links. For example, the two words can be both part of a proverb (e.g. bird and world in the proverb “early bird catches the world”), of a film title (e.g. river and return in “River of No Return”), or they can be linked semantically (e.g. Suarez and bite because of Suarez’s bite to Chiellini during the 2014 World Cup). The task of solving this game was presented as the NLP4FUN task of Evalita 2018 (Basile et al., 2018). To build our system, first, we collected and analyzed a corpus of 296 game instances: 146 from the TV show and 150 from the board game. Second, we built an association matrix launching a lexicalized association rules algorithm, developed by us, on Paisà (Lyding et al., 2014). Then, we collected from the web a list of titles of books, films, plays, and songs; and a list of proverbs. Finally, we tested the system on the game instances collected and we compared it with other artificial players of “La Ghigliottina”, especially UNIOR4NLP (Sangati, Pascucci and Monti, 2018), that obtained the best performance on this task at Evalita 2018 (Basile et al., 2018).

Conclusion
We described and tested Robospierre, a system developed to solve the word game “La Ghigliottina” (the guillotine). The result of the tests showed that, even if its result were below state-of-the-art, it was able to solve some game instances that the state-of-the-art system did not solve. In the future, we plan to improve the extraction of the links in the MWEs by extracting them from a bigger corpus. We also intend to assign at every element in the list of proverbs and in the lists of titles a score that represents how much that element is known.
Is “manovra” Really “del popolo”? Linguistic Insights into Twitter Reactions to the Annual Italian Budget Law
Authors: Claudia Roberta Combei (Università di Bologna, 2019)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Relying on linguistic cues obtained by means of structural topic modelling as well as descriptive lexical analyses, this study contributes to the general understanding of the Twitter users’ response to the annual Italian budget law approved at the end of December 2018. Some topics contained in the dataset of tweets are procedural or generic, but besides those, it often emerges that Twitter users expressed their concern with respect to the provisions of this law. Supportive attitudes seem to be less frequent. This paper also advocates that findings from inductive studies on Twitter data should be interpreted with caution, since the nature of tweets might not be adequate for drawing far-reaching generalisations.

Introduction
In the last decade, the internet has revolutionized human communication and interaction. Among all forms of digitally-mediated communication, social media stand out as one of the most effective. As Boulianne (2017) points out, the effects of social media depend on their nature of use (e.g. source of information; one-to-one/one-to-many/many-to-many communication; networking and relationship-building; expression of opinions; etc.). Nowadays, potentially everyone with a computer or a mobile device having access to the internet can write and share content which may be viewed and debated immediately by other people. The impact of a social media post may be huge, and unlike other prior forms of communication, it can easily cross borders in just a few seconds. In fact, social media make things happen faster than ever before. For instance, Facebook and Twitter were crucial in allowing the Arab uprisings or the Romanian anti-corruption protests to happen more efficiently and on a larger scale.

Conclusion
This paper explored the Twitter users’ reactions to the annual Italian budget bill. STM outputs and descriptive lexical analyses showed that tweets concerned various aspects associated with the object of this study. Apart from talking about procedural and generic issues, users expressed their doubts and disapproval with respect to the measures of the budget law. Generally, tweets supporting this law were less frequent. The findings of this study, although preliminary, might be seen as indicators of what subsequently turned out to be a failure for the first Conte government. Still, as reiterated throughout the paper, the results might not reflect the real attitudes of the Italian voting population, since Twitter users tend to be younger and to have an above-average level of education and political sophistication (cf. Barberá et al., 2015). Moreover, tweets, by nature, might not be suitable for drawing steady generalizations, even if the prospects they offer for content and discourse analysis are indeed significant. Further research on this topic might include the investigation of Twitter users’ reactions by means of sentiment analysis.
Fixing Comma Splices in Italian with BERT
Authors: Daniele Puccinelli, Silvia Demartini, Renée E. D'Aoust (University of Applied Sciences and Arts of Southern Switzerland, North Idaho College, 2019)
Topic: Language Models

Abstract
We propose a fully unsupervised strategy to fix comma splices. Leveraging the pre-training of Bidirectional Encoder Representations from Transformers (BERT), our strategy is to mask out commas and let BERT guess what to replace them with. Our strategy achieves promising results on a challenging targeted corpus of awkwardly worded sentences from Italian-language college student essays.

Introduction
Comma splices can be defined as independent clauses joined by a comma without a coordinating conjunction (Hacker, 2009). Comma splices are frequent in both English and Italian and typically suggest a lack of basic understanding of sentence structure. As we will show, they come in various flavors, and there exist subtle differences between how they occur in English and Italian. Comma splices are generally detected by commercial grammar and style checkers, but their automated correction has only been addressed by a few studies specific to English. Because the common denominator shared by such studies is the use of supervised machine learning techniques, the key research question that motivated the present study is whether we can use transfer learning to correct comma splices automatically in a completely unsupervised fashion and in languages other than English. Thanks to contextualized word embeddings, and, in particular, thanks to BERT (Devlin et al., 2019), we show that it is possible to correct common cases of comma splices in Italian. We also discuss the limitations of our unsupervised approach.

Conclusion
While the main limitation of the present study is the limited size of the USS corpus, we believe that the challenging nature of the writing excerpts in the USS corpus has enabled us to stress-test our strategy and to deliver a solid proof of concept that leverages the power of BERT-style contextualized word embeddings for automated style correction. Our future plans include using our BERT-based strategy to correct comma splices in English-language L1 and L2 student writing and to correct runon sentences.
Surviving the Legal Jungle: Text Classification of Italian Laws in Extremely Noisy Conditions
Authors: Riccardo Coltrinari, Alessandro Antinori, Fabio Celli (Università di Camerino, Gruppo Maggioli, 2020)
Topic: In-domain IR and IE

Abstract
In this paper, we present a method based on Linear Discriminant Analysis for legal text classification of extremely noisy data, such as duplicated documents classified in different classes. The results show that Linear Discriminant Analysis obtains very good performances both in clean and noisy conditions, if used as classifier in ensemble learning and in multi-label text classification.

Introduction
We address text categorization of businessoriented legal documents in Italian, but with a custom and overlapping hierarchy of product categories. A typical approach to tackle similar tasks is to exploit resources such as EUROVOC (Daudaravicius, 2012), a multilingual thesaurus consisting of over 6700 hierarchically-organised class descriptors used by many organizations of the European Union (EU) for the classification and retrieval of official documents. Our editorial system has a hierarchy of 23 product categories and more than 20600 labels, manually annotated and customized for different clients in more than 15 years, hence it is not possible to exploit resources like EUROVOC to categorize documents. In this paper, we propose a fast and efficient method for document classification for noisy data based on Linear Discriminant Analysis, a dimensionality reduction technique that has been employed successfully in many domains, including neuroimaging and medicine. We believe that our contribution will be useful to the NLP community in the context of document categorization as well as automatic ontology population, in particular when dealing with very noisy data. The paper is structured as follows: in Section 1.1 we present the related works in the field of text classification and the potential of Linear Discriminant Analysis, in Section 2 we describe the datasets we used, in Section 3 we report and discuss the result of our classification experiments and in Section 4 we draw our conclusions.

Conclusion
We experimented with various settings, feature selection methods and classification algorithms, and we found a method to extract good models in extremely noisy conditions, even with documents repeated under different labels. LDA proved to be a valuable classification and feature selection technique, but we obtained the best performances when LDA is combined with other algorithms. The results we obtained with the score ranking classification are in line with the state-of-the-art, but our method is more suitable for small and noisy datasets. In the future we plan to apply the score ranking algorithm on a larger dataset and to use it in a real multi-label environment comparing the results with the state-of-the-art of Extreme Multi-Label Document Classification AUTHOR. We also plan to make comparisons with the more recent state of the art deep learning techniques and to apply semantic indexing to the documents to check for improvements.
MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian
Authors: Rachele Sprugnoli (Università Cattolica del Sacro Cuore, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper presents a new linguistic resource for Italian, called MultiEmotions-It, containing comments to music videos and advertisements posted on YouTube and Facebook. These comments are manually annotated according to four different dimensions: i.e., relatedness, opinion polarity, emotions and sarcasm. For the annotation of emotions we adopted the Plutchik's model taking into account both basic and complex emotions, i.e. dyads.

Introduction
Emotions play an influential role in consumer behaviour affecting the decision to purchase goods and services of different types, including music AUTHOR. Both positive and negative emotions have an influence and this is why marketing strategies have always focused on both rational and emotional aspects AUTHOR. With the advent of social media, platforms such as YouTube and Facebook have gained importance in the marketing industry because they allow to connect and engage consumers AUTHOR. The progressive consolidation of social media as marketing spaces has highlighted the need to monitor unstructured data written by social media users. In this context, the application of Sentiment Analysis techniques have flourished with the aim of tracking customers’ opinions and attitudes by analysing comments or reviews posted on social media channels AUTHOR. In this paper we present a new linguistic resource for Italian, called MultiEmotions-It, containing comments to music videos and advertisement posted on YouTube and Facebook. Comments are manually annotated according to four different dimensions: relatedness, opinion polarity, emotions and sarcasm. Particular attention is devoted to the annotation of emotions for which we adopted the model proposed by Plutchik AUTHOR. Following Plutchik, we take into consideration both the eight basic emotions (joy, sadness, fear, anger, trust, disgust, surprise, anticipation) and the dyads, that is feelings composed of two basic emotions (e.g., love is a blend of joy and Trust). At the time of writing, MultiEmotions-It is the only freely available manually annotated dataset for emotion analysis for Italian.

Conclusion
This paper describes MultiEmotions-It, a new manually annotated dataset for opinion polarity and emotion analysis made of more than 3,000 comments on music videos and advertisements published on YouTube and Facebook. As for future work, we plan to: (i) extend the annotation guidelines to distinguish the specific object towards which the opinion is directed (e.g. the product, the actor, the location of the video) following the work by Severyn et al. (2016), (ii) extend the dataset with new comments taken also from Instagram and Twitter, (iii) extract a new word-emotion association lexicon from MultiEmotions-It using vector space models (Passaro et al., 2015) in order to cover complex emotions.
Becoming JILDA
Authors: Irene Sucameli, Alessandro Lenci, Bernardo Magnini, Maria Simi, Manuela Speranza (Università di Pisa, Fondazione Bruno Kessler, 2020)
Topic: Chatbots and Dialogue Systems

Abstract
The difficulty in finding useful dialogic data to train a conversational agent is an open issue even nowadays, when chatbots and spoken dialogue systems are widely used. For this reason we decided to build JILDA, a novel data collection of chat-based dialogues, produced by Italian native speakers and related to the job-offer domain. JILDA is the first dialogue collection related to this domain for the Italian language. Because of its collection modalities, we believe that JILDA can be a useful resource not only for the Italian research community, but also for the international one.

Introduction
Chatbots and spoken dialogue systems are now widespread; however, there is still a main issue connected to their development: the availability of training data. Finding useful data to train a system to interact as human-like as possible is not a trivial task. This problem is even more critical for the Italian language, where only few datasets are available. To supplement this deficiency of data, we decided to develop JILDA (Job Interview Labelled Dialogues Assembly), a new collections of chat-based mixed-initiative, human-human dialogues related to the job offer domain. Our work offers different elements of novelty. First of all, it constitutes, to the best of our knowledge, the first dialogue collection for this domain for the Italian language. Moreover, our dataset was not built using a Wizard of Oz approach, usually adopted in the realization of dialogues. Instead, we used an approach similar to the Map Task one, as we will describe in the next section. This allowed us to obtain more complex, mixed-initiative dialogues.

Conclusion
In this paper we presented JILDA, a novel dataset of chat-based, mixed-initiative dialogues built for the Italian language and related to the job-offer domain. This new resource has been built adopting an experimental approach based on the Map Task experiment. This has allowed us to collect mixedinitiative data which represent effectively the naturalness which is typical in the human-human interaction. The JILDA dataset, which includes 525 dialogues, is in the process of being completely annotated with dialogue acts and entities related to this specific domain. For the annotation of those dialogues we are using our own extension of LIDA. The annotated dialogues will then be used to train a conversational agent. Thanks to this new resource, our goal is to allow an agent chat with the user in a natural and human-like way.