alemiaschi/clic-it_corpus huggingface dataset:
Dialog-based Help Desk through Automated Question Answering and Intent Detection
Authors: Antonio Uva, Pierluigi Roberti, Alessandro Moschitti (Università di Trento, Im Service Lab Srl, 2020)
Topic: Chatbots and Dialogue Systems

Abstract
Modern personal assistants require to access unstructured information in order to successfully fulfill user requests. In this paper, we have studied the use of two machine learning components to design personal assistants: intent classification, to understand the user request, and answer sentence selection, to carry out question answering from unstructured text. The evaluation results derived on five different real-world datasets, associated with different companies, show high accuracy for both tasks. This suggests that modern QA and dialog technology is effective for real-world tasks.

Introduction
Help-desk applications use Machine Learning to classify user's request into intents. The information owned by companies generally is in free text form, from company's documents or websites. For example, corporate knowledge is typically encoded within documents in an unstructured way. This poses limitations on the effectiveness of standard information access. For example, searching documents by keywords is not a viable solution for the users, as they seldom can find an answer to their questions. The possibility of using QA systems to search for information on a corpus of documents, also through a dialogue system, offers an attractive solution for extracting the best information from the company knowledge bases. IMSL company offers virtual agents that can be retrained based on the customer needs. The agent is composed of many Natural Language Understanding components, such as classifiers that map each user utterance in input to their corresponding intent. However, since it is not possible to forecast all the intents corresponding to the questions that the user are going to ask -- which are potentially infinite -- it is of paramount importance to have an automated QA system able to automatically provide the best answer (paragraph) extracted from a company owned knowledge base. Information access is becoming an increasingly critical issue. Traditional Information Retrieval systems, used in industry, help the user in accessing information, but are often imprecise and impractical. Current search engines are an example of this. Searching for information on the web often requires a double effort for the user: first it is necessary to understand how to formulate a query in the most effective manner, and then filter out the proposed results in order to find the most relevant information. In this paper, we described our QA system based on answer sentence selection and intent detection, and how we integrate them in a Conversational agent.

Conclusion
In this paper, we have presented a modern dialog system for real-world applications. We have tested advanced technology for QA and intent classification on several datasets derived from company data, such as Banks and Credit Institutions. The results show a promising direction for SMEs to build their own effective access to unstructured data.
It Is MarkIT That Is New: An Italian Treebank of Marked Constructions
Authors: Teresa Paccosi, Alessio Palmero Aprosio, Sara Tonelli (Fondazione Bruno Kessler, 2021)
Topic: Syntax and Dependency Treebanks

Abstract
In this paper we present MarkIT, a treebank of marked constructions in Italian, containing around 800 sentences with dependency annotation. We detail the process to extract the sentences and manually correct them. The resource covers seven types of marked constructions plus some ambiguous sentences, whose syntax can be wrongly classified as marked. We also present a preliminary evaluation of parsing performance, comparing a model trained on existing Italian treebanks with the model obtained by adding MarkIT to the training set.

Introduction
In recent years, the goal to develop robust frameworks for consistent annotation of syntactic dependencies across different human languages has led to the creation of Universal Dependencies (UD), an initiative covering nearly 200 treebanks in more than 100 languages. Since UD treebanks are then used to train syntactic parsers, it is important that they account for as many phenomena as possible that can be found in a language, and not only for canonical expressions typically written in news. The purpose to encompass the variety of use in the Italian language has been pursued by including different genres in the VIT treebank <cit.> and in ParTUT <cit.> and more recently by including syntactically annotated tweets <cit.> in the UD framework. Overall, seven treebanks are listed under the UD initiative for Italian. In this work, we contribute to this effort by presenting a novel treebank including syntactically annotated marked constructions, which we call MarkIT (MARKed structures Italian Treebank). The samples have been extracted from a corpus of students' essays and to our knowledge represents the first effort to include in UD a repository of marked structures, which are typical of neo-standard language and are therefore more and more frequent in informal settings <cit.>. The sentences have been first syntactically parsed and then manually corrected, so that we were also able to analyse which kinds of mistakes are typically done by dependency parsers.
The dataset is freely available on Github at <https://github.com/dhfbk/markit>.

Conclusion
In this work we present MarkIT, a novel treebank composed of 800 sentences with syntactic annotation of marked structures. The resource covers seven types of marked sentences, plus around 200 sentences whose structure is not marked but that may be misleading both for parsers and for human annotators. The treebank is made available to the community and is meant to make dependency parsers more robust to the different syntactic structures present in Italian, in particular in the neo-standard variant. The work is still in progress, since we plan to add to the resource other sentences from the IPRASE corpus. Our goal is to include all marked sentences present in the essays, so to analyse also the distribution of the different sentence structures in this type of texts.
No Place For Hate Speech @ AMI: Convolutional Neural Network and Word Embedding for the Identification of Misogyny in Italian
Authors: Adriano dos S. R. da Silva, Norton T. Roman (University of Sao Paulo, 2021)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper, we address the problem of automatic misogyny identification focusing on understanding the representation capabilities of widely adopted embeddings and addressing the problem of unintended bias. The proposed framework, grounded on Sentence Embeddings and Multi-Objective Bayesian Optimization, has been validated on an Italian dataset. We highlight capabilities and weaknesses related to the use of pre-trained language, as well as the contribution of Bayesian Optimization for mitigating the problem of biased predictions. 

Introduction
Nowadays, although women, girls and teenagers have a strong presence in online social environments, they are strongly exposed to hateful comments. In 2021, a survey provided by the Pew Research Center has shown that females are targeted for severe types of online gender-based attacks[https://www.pewresearch.org/internet/2021/01/13/the-state-of-online-harassment/]: women are more likely than men to report having been sexually harassed online (16% vs. 5%) or stalked (13% vs. 9%). These phenomena can be found under the umbrella of online misogyny, which can be generally defined as hate, violence or prejudice against women <cit.>.

Conclusion
In this paper we have investigated the capabilities and weaknesses of pre-trained language models, as well as the problem of the unintended bias when addressing the automatic misogyny identification for the Italian language. The proposed investigation has highlighted that, while pre-trained embeddings are able to distinguish misogynous and not misogynous comments, they still have poor discrimination capabilities related to the type of misogyny and its target. Regarding the unintended bias problem, it has been shown that an hyper-parameter search guided by Bayesian Optimization can lead to debiased models with good recognition generalization capabilities. As future work, we will investigate explainable AI techniques aimed at generating a feature score that is directly proportional to the feature's effect on inducing bias in the prediction model.
“That branch of the Lake of Como...”: Developing a New Resource for the Analysis of I Promessi Sposi and its Historical Translations
Authors: Rachele Sprugnoli, Marco Sartor (Università di Parma, 2023)
Topic: Text Simplification

Abstract
This paper presents a directional parallel corpus of the Ventisettana, the version of I Promessi Sposi published by Manzoni in 1827, aligned at the sentence level with the anonymous English translation published in London in 1834 by Richard Bentley. After describing the procedure followed for creating the resource and analyzing the results of the manual alignment, the corpus is used as a gold standard to evaluate the Bertalign automatic aligner. This new linguistic resource can benefit the research community, particularly in the fields of the history of literature and translation studies, and be useful for developing new automatic tools specific for handling the peculiarities of historical literary texts.

Introduction
This paper describes the creation of a sentence-level parallel corpus comprising the full text of the Ventisettana—the version of the novel published by Manzoni in 1827—and the anonymous English translation from 1834. The resource is available on GitHub in XLM format and will also be hosted in the ILC4CLARIN repository. The entire aligned corpus has been used as a gold standard to evaluate Bertalign, an embedding-based automatic sentence aligner. Results obtained with a custom parameter setting are compared to those achieved using default settings and a length-based algorithm (Gale-Church), showing very good performance with an F1 score slightly below 0.9. This work serves as a foundation for future experiments on other editions of the novel and its main translations into neo-Romance languages. A sentence-level alignment for Chapter VIII is underway, incorporating the widest possible selection of English translations, including a recent American version produced through collaboration with the translator. Keeping sentence unity in the Italian text aids in comparing translations and examining translators' choices from a diachronic perspective. A word-level alignment of select chapters from the Ventisettana with the 1834 English edition is also in progress, performed using Ugarit. Unlike other projects aimed at building translation memories for machine translation, this project focuses on analyzing the translator's decisions. Alignments are carefully conducted, considering punctuation and non-literal correspondences, to highlight oversights, errors, and unique innovations in translation. The results of our manual alignment will be used to evaluate automatic approaches such as fast_align and AWESOME.

Conclusion
Annotating Homeric Emotions by a Domain-Specific Language
Authors: Federico Boschetti, Laura Chilla, Maria Konstantinidou, John Pavlopoulos (CNR-ILC, Univeristà Ca’ Foscari Venezia, Democritus University of Thrace, Athens University of Economics and Business, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this study, we introduce a novel approach to understanding the emotive content in ancient literature, with a specific focus on the first book of Homer’s Iliad. Recognizing the challenges inherent in interpreting emotions from ancient texts, we developed a Domain-Specific Language (DSL) tailored for this purpose. This DSL facilitates the annotation of basic sentiments—positive, negative, neutral, or mixed—while also enabling the identification and categorization of specific emotions. To ensure relevance and applicability, we mapped the extracted emotions to authoritative domain ontologies, bridging the gap between ancient emotional expressions and contemporary understanding. Our preliminary results, discussed in detail, demonstrate the potential of our approach in providing deeper insights into the emotional landscape of ancient texts. We believe this methodology can serve as a foundation for future studies aiming to decode emotions in historical literature, opening new avenues for interdisciplinary research in humanities and computational linguistics.

Introduction
This study builds upon the investigations of Pavlopoulos [1] regarding the annotation of sentiment and emotions in the first book of Homer’s Iliad, translated into modern Greek. In this second phase, the focus shifts to analyzing the original ancient Greek text, emphasizing the expressiveness of the annotation system in capturing multiple aspects [2] of the observed textual units. As in the prior work, annotators are tasked with indicating both sentiment (positive, negative, neutral, or mixed) and specific emotions (from an open set of possibilities). However, they can annotate at varying levels of granularity—ranging from a single word to several verses—addressing both paradigmatic (words outside context) and syntagmatic (textual units within context) dimensions. Additionally, the annotations consider different perspectives, such as those of characters and the ancient audience, and their emotions toward various participants in the scene.

Conclusion
This study introduced a domain-specific language for annotating the emotive content of the first book of Homer’s Iliad. We mapped the emotions extracted from the annotations and analyzed the results. Future work includes studying the polarity of additional emotions, exploring verses with contradictory polarities, and performing sentiment analysis based on the subject’s role (annotator, character, or narrator). Furthermore, we plan to involve more annotators to measure inter-annotator agreement and investigate verses that elicit consistent or diverse emotional responses among different annotators.
The PBSDS: A Dataset for the Detection of Pseudoprofound Bullshit
Authors: Evan DeFrancesco, Carlo Strapparava (Università di Trento, Fondazione Bruno Kessler, 2023)
Topic: Fact Checking and Fake News Detection

Abstract
This paper introduces the PBSDS, a dataset of tweets containing pseudoprofound bullshit—statements designed to appear profound but lacking substantive meaning. The PBSDS serves as a resource for studying pseudoprofound bullshit, exploring potential linguistic factors in perceiving bullshit. The dataset’s creation and experiments with classifiers show promising results, despite limitations such as selection bias and subjective annotation.

Introduction
“Bullshit" refers to communication that is designed to impress but is constructed without concern for truth [1]. Bullshit differs from lying in that the liar deliberately manipulates and subverts truth (usually with the intent to deceive), while the bullshitter is simply unconcerned with what is true and what is false. A liar needs to know the truth value of a proposition; the bullshitter simply does not care. Although bullshit comes in different forms, in this project, we focused specifically on what is referred to as “pseudoprofound bullshit," which is designed to convey some sort of potentially profound meaning but is actually semantically vacuous [2], e.g., “Hidden meaning transforms unparalleled abstract beauty." Table 1 reports further examples of pseudoprofound bullshit and nonpseudoprofound bullshit sentences from our dataset. The goal of this project is to construct a dataset of tweets that contain pseudoprofound bullshit in English (the PBSDS). Operating under the assumption that bullshit is similar to spam email, we hypothesize that it should be possible to detect pseudoprofound bullshit using relatively simple classification algorithms.

Conclusion
Despite its limitations, the PBSDS offers valuable insights into the phenomenon of pseudoprofound bullshit and its detection. The dataset provides a foundation for further research, enabling comprehensive investigations into linguistic patterns, cognitive biases, and societal implications associated with pseudoprofound bullshit. By better understanding and identifying pseudoprofound bullshit, researchers can develop tools and strategies to enhance critical thinking, combat deceptive communication, and promote media literacy in an increasingly complex information landscape.
An Italian Verb Lexicon for Sentiment Inference
Authors: Manfred Klenner, Dylan Massey, Susanna Tron (University of Zurich, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Verb-centered sentiment analysis provides a fine-grained perspective on the polar impact a situation has on its participants. It is good for a person to be honored for e.g. some achievement and we know in advance that such an achievement is expected to have positive polarity. Also, there is a positive relation between the honorer and the honoree. We introduce an Italian verb lexicon that specifies the polar relations a verb expresses and the effects and expectations that the roles of the verb bear.

Introduction
A sentence is not just positive or negative, it moreover sheds a (polar) light on its participants. Given a sentence like I mourn my freedom (It. Piango la mia libertà), a negative effect is cast on the subject and we expect a positive filler of the direct object. Furthermore, we might conclude that the subject is in favour of the object. We have created an Italian verb lexicon comprising 307 verbs with up to 3 frames per verb in order to enable such kind of inferences. Such a lexicon might be useful for stance detection or even for lexicon induction (e.g. from the object expectation of mourn we can conclude that libertà is a positive concept). In this short paper, we discuss the ingredients of our lexicon both from a practical and theoretical point of view.

Conclusion
In this paper, we have described an Italian verb lexicon and the theory behind it. The lexicon specifies for each Italian verb the underlying relation (in favour or against) and for each verb argument the effects and expectations (if any). The resource has been released together with a traditional polarity lexicon (nouns, adjectives). The verb lexicon can be used for sentiment inference, finding out who is in favour of or against who or what. This is useful e.g. for the profiling of political parties: are they in favour or against some topic. Also, the polar perspective which we call effects can be determined: to answer the question whether a text is positive or negative towards an animate or inanimate object. This enables to determine the stance of a text author, but also can be used in order to find out how a person or a product is framed e.g. in a particular text or text collection.
Textual Entailment with Natural Language Explanations: The Italian e-RTE-3 Dataset
Authors: Andrea Zaninello, Sofia Brenna, Bernardo Magnini (Fondazione Bruno Kessler, Libera Università di Bolzano, 2023)
Topic: Language Models

Abstract
We introduce the ’e-RTE-3-it’ dataset, an enriched version of the Italian RTE-3 dataset, where each text-hypothesis pair, in addition to the ’entailment’, ’contradiction’, or ’neutrality’ label, has been enriched with an explanation for the label itself. Moreover, the dataset includes the level of confidence with which the annotators could write the explanation, and in cases where the annotators did not agree with the original label, an alternative label, along with an explanation for the new label. This offers the opportunity to analyse cases of uncertainty in annotation and delve into different perspectives on language understanding.

Introduction
Recently, Large Language Models (LLMs) like T5 [1], GPT-3.5/4 [2], LLama-2 [3], It5 [4], and Camoscio [5] have demonstrated impressive performance across various natural language processing tasks. Despite their success, these LLMs also face limitations and risks, such as lack of factuality [6], hallucinations [7], and poor transparency [8]. As a result, there is a growing demand for "inherent explainability," which refers to the ability of models to provide human-like, natural language explanations for their predictions. Many studies have thus focused on natural language explanations, and numerous datasets have been created for this purpose, primarily in English [9]. However, there is a notable gap for non-English languages, including Italian. To fill this void, this paper introduces the ’e-RTE-3-it’ dataset, the first Italian dataset for natural language inference enriched with free-form, human-written explanations for the relationship between two sentences. Additionally, the dataset includes alternative labels and confidence scores from annotators to account for the variability in human judgments. This aspect of the annotation scheme enhances the ’e-RTE-3-it’ dataset, making it a valuable resource for exploring subjectivity and variability in language understanding.

Conclusion
The insights derived from the ’e-RTE-3-it’ dataset pave the way for multifaceted research directions. The provided explanations can serve as a gold standard for training models to generate human-like explanations. Further, the alternative labels and explanations open avenues for investigating the subjectivity in language understanding. The rich layers of the dataset also allow for the study of correlation between the original and alternative labels, the confidence score, and the degree of disagreement among annotators. Future work includes utilizing the data to develop models capable of providing explanations for their entailment decisions and conducting a deeper analysis into the dynamics of subjectivity in the entailment task.