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3. The Efficiency of Question‐Asking Strategies in a Real‐World Visual Search Task.

Author

Testoni, Alberto, Bernardi, Raffaella, and Ruggeri, Azzurra

Subjects
*VISUAL perception, *NATURAL language processing, *ARTIFICIAL neural networks, *COMPUTER vision, *VERBAL behavior
Abstract

In recent years, a multitude of datasets of human–human conversations has been released for the main purpose of training conversational agents based on data‐hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine‐grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question‐asking strategies of human participants by employing different variants of the so‐called 20‐question‐game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20‐question‐game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real‐world images, making it a suitable setting to investigate the question‐asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question‐asking strategies on large‐scale datasets, while at the same time posing new questions about the development of conversational systems. [ABSTRACT FROM AUTHOR]

Published
2023
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9. Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

Author

Altuna, Begoña, Alzetta, Chiara, Anselma, Luca, Antinori, Alessandro, Aprosio, Alessio Palmero, Ardito, Luca, Argese, Chiara, Bacco, Luca, Baisa, Vít, Balaraman, Vevake, Barrón-Cedeño, Alberto, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Bassignana, Elisa, Benvenuti, Nicola, Bernardi, Raffaella, Bertino, Enrico, Biasion, Davide, Biffi, Marco, Bolioli, Andrea, Bonadiman, Daniele, Bonora, Paolo, Bosca, Alessio, Bosco, Cristina, Brambilla, Silvia, Brunato, Dominique, Bucur, Ana-Maria, Cafagna, Michele, Caligiore, Gaia, Cappa, Claudia, Caputo, Annalina, Carlino, Carola, Caselli, Tommaso, Cassotti, Pierluigi, Castagnoli, Sara, Casula, Camilla, Cecchini, Flavio M., Celli, Fabio, Cervone, Alessandra, Chiusaroli, Francesca, Chung, Yi-Ling, Cignarella, Alessandra Teresa, Cimino, Andrea, Colla, Davide, Coltrinari, Riccardo, Colucci, Ilaria, Corino, Elisa, Croce, Danilo, Dell’Orletta, Felice, Delsanto, Matteo, de Gemmis, Marco, De Mattei, Lorenzo, de Varda, Andrea Gregor, Dinu, Liviu P., di Buono, Maria Pia, Di Gangi, Mattia Antonino, Di Lascio, Mirko, Ducret, Martina, Fabris, Alessandro, Falco, Mariacristina, Favalli, Andrea, Favaro, Manuel, Feldman, Anna, Fernández, Raquel, Fernicola, Francesco, Ferro, Marcello, Franzini, Greta, Gabrieli, Giuliano, Gagliardi, Gloria, Gaido, Marco, Gandolfi, Greta, Garcea, Federico, Gatt, Albert, Gemmis, Marco de, Giannone, Cristina, Giulivi, Sara, Gregori, Lorenzo, Gualdoni, Eleonora, Guerini, Marco, Hoste, Veronique, Iavarone, Benedetta, Jezek, Elisabetta, Karakanta, Alina, Kopp, Stefan, Kruse, Lauren, Lavelli, Alberto, Lenci, Alessandro, Liello, Luca Di, Lim, Alfred, Lops, Pasquale, Louvan, Samuel, Magnini, Bernardo, Mambrini, Francesco, Mana, Dario, Manna, Raffaele, Marino, Gian Manuel, Martinez, Carlos, Marzi, Claudia, Masini, Francesca, Mattei, Andrea, Mazzei, Alessandro, Menini, Stefano, Mensa, Enrico, Merone, Mario, Miaschi, Alessio, Micheli, M. Silvia, Montemagni, Simonetta, Monti, Johanna, Moretti, Giovanni, Morisio, Maurizio, Moschitti, Alessandro, Muffo, Matteo, Musto, Cataldo, Nadalini, Andrea, Negri, Matteo, Nissim, Malvina, Nolano, Gennaro, Nuovo, Elisa Di, Oliveri, Isabeau, Onnis, Luca, Origlia, Antonio, Osenova, Petya, O’Brien, Beth A., Palmero Aprosio, Alessio, Papa, Sirio, Pascucci, Antonio, Passarotti, Marco, Patti, Viviana, Paulon, Luca, Pellegrini, Matteo, Pelosi, Serena, Peng, Jing, Pezzelle, Sandro, Pierucci, Maria Laura, Pirinen, Tommi A., Pirrelli, Vito, Polignano, Marco, Radicioni, Daniele P., Ravelli, Andrea Amelio, Rescigno, Argentina Anna, Riccardi, Giuseppe, Rizzo, Giuseppe, Roberti, Pierluigi, Roccabruna, Gabriel, Rodella, Anna, Romagnoli, Raniero, Romani, Emma, Ruggiero, Gaetana, Sanguinetti, Manuela, Sarti, Gabriele, Semeraro, Giovanni, Silvello, Gianmaria, Simeoni, Rossana, Simi, Maria, Simoniello, Vincenzo, Simov, Kiril, Speranza, Giulia, Speranza, Manuela, Spillo, Giuseppe, Sprugnoli, Rachele, Strapparava, Carlo, Sucameli, Irene, Suozzi, Alice, Susto, Gian Antonio, Tamburini, Fabio, Taxitari, Loukia, Tekiroğlu, Serra Sinem, Testoni, Alberto, Tonelli, Sara, Tripodi, Rocco, Trosterud, Trond, Turchi, Marco, Uva, Antonio, Vanmassenhove, Eva, Varvara, Rossella, Vassallo, Marco, Venturi, Giulia, Vigorelli, Pietro, Vitale, Pierluigi, Way, Andy, Wiechetek, Linda, Yavuz, Mehmet Can, Zampedri, Federica, Zaninello, Andrea, Zanoli, Roberto, Zarino, Wanda Punzi, Zhang, Shibingfeng, Dell'Orletta, Felice, Monti, Johanna, and Tamburini, Fabio

Subjects
Twitter during Pandemic, Automatic Sarcasm Detection, Linguistic Ostracism in Social Networks, AriEmozione, COVID-19, Linguistics, LAN000000, Quantitative Linguistic Investigations, Fine-grained sentiment analysis, Online Hate Speech, Computational Linguistics, DistilBERT, Depression from Social Media, Distributional Semantics, Gender Bias, CBX, AEREST, E3C Project, Multilingual NLU, TrAVaSI
Abstract

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six 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.

Published
2021

12. Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

Author

Abramova, Ekaterina, Adorni, Giovanni, Agrawal, Ruchit, Aina, Laura, Albanese, Teresa, Albanesi, Davide, Alzetta, Chiara, Amore, Matteo, Antonelli, Oronzo, Aprosio, Alessio Palmero, Balaraman, Vevake, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Bassignana, Elisa, Bellandi, Andrea, Bentivogli, Luisa, Bernardi, Raffaella, Bertoldi, Nicola, Bondielli, Alessandro, Bos, Johan, Bosco, Cristina, Bottini, Roberto, Brunato, Dominique, Brunato⋄, Dominique, Büchler, Marco, Buono, Maria Pia di, Busso, Lucia, Cabrio, Elena, Caruso, Valeria, Caselli, Tommaso, Cecchini, Flavio, Celli, Fabio, Cervone, Alessandra, Chesi, Cristiano, Chingacham, Anupama, Chiriatti, Giulia, Cimino, Andrea, Cocciu•, Eleonora, Colla, Davide, Comandini, Gloria, Cordeiro, Silvio Ricardo, Crepaldi, Davide, Croce, Danilo, Curtoni, Paolo, Cutugno, Francesco, dell’Oglio, Pietro, Dell’Orletta, Felice, Dell’Orletta⋄, Felice, De Felice, Irene, De Martino, Maria, Dini, Luca, Di Iorio, Angelo, Di Nunzio, Giorgio Maria, Draetta, Lia, Ducceschi, Luca, Elia, Annibale, Falavigna, Daniele, Federico, Marcello, Feltracco, Anna, Fernández, Raquel, Ferro, Michele, Fieromonte, Martina, Franzini, Greta, Gagliardi, Gloria, Gala, Valentina Della, Gambi, Enrico, Ghezzi, Ilaria, Giovannetti, Emiliano, Gobbi, Jacopo, Gretter, Roberto, Guarasci, Raffaele, Guerini, Marco, Günther, Fritz, Gurevych, Iryna, Herzog, Leonardo, Jezek, Elisabetta, Koceva, Forsina, Lai, Mirko, Laudanna, Alessandro, Lenci, Alessandro, Lepri, Bruno, Liano, Annarita, Limpens, Freddy, Louvan, Samuel, Lyding, Verena, Magnini, Bernardo, Magnolini, Simone, Mairano, Paolo, Mambrini, Francesco, Mana, Dario, Mancuso, Azzurra, Marchi, Simone, Marelli, Marco, Marini, Costanza, Mazzei, Alessandro, McGregor, Stephen, Melnikova, Elena, Menini, Stefano, Mensa, Enrico, Merenda, Flavio, Mollo, Eleonora, Montemagni, Simonetta, Montemagni⋄, Simonetta, Monti, Johanna, Moretti, Giovanni, Moritz, Maria, Nadalini, Andrea, Negri, Matteo, Nicolas, Lionel, Nissim, Malvina, Novielli, Nicole, Okinina, Nadezda, Pannitto, Ludovica, Paperno, Denis, Passalacqua, Samuele, Passaro, Lucia C., Passarotti, Marco, Patti, Viviana, Pecchioli, Alessandra, Pellegrini, Matteo, Petrolito, Ruggero, Pettenati, Maria Chiara, Piantanida, Giovanni, Poggi, Isabella, Porporato, Aureliano, Quinci, Vito, Radicioni, Daniele P., Ramisch, Carlos, Rapp, Amon, Riccardi, Giuseppe, Rossini, Daniele, Rotondi, Agata, Ruffolo, Paolo, Russo, Irene, Sagri, Maria Teresa, Sangati, Federico, Sanguinetti, Manuela, Savary, Agata, Savy, Renata, Simeoni, Rossana, Simi, Maria, Sorgente, Antonio, Speranza, Manuela, Sprugnoli, Rachele, Stede, Manfred, Stepanov, Evgeny A., Stingo, Michele, Tamburini, Fabio, Tebbifakhr, Amirhossein, Tonelli, Sara, Torre, Ilaria, Tortoreto, Giuliano, Totis, Pietro, Trotta, Daniela, Turchi, Marco, Valeriani, Martina, Venturi, Giulia, Venturi⋄, Giulia, Vezzani, Federica, Villata, Serena, Vincze, Veronika, Zaghi, Claudia, Zovato, Enrico, Cabrio, Elena, Mazzei, Alessandro, and Tamburini, Fabio

Subjects
elaborazione del linguaggio naturale, Computational Linguistics, History & Philosophy Of Science, analisi semantica, CBX, Gurevych (Iryna), Bos (Johan), LAN000000, linguistica computazionale, Natural Language Processing, semantic parsing
Abstract

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.

Published
2019

13. Formal Grammar: 24th International Conference, FG 2019, Riga, Latvia, August 11, 2019, Proceedings

Author

Bernardi, Raffaella, Kobele, Greg, Pogodalla, Sylvain, University of Trento [Trento], Universität Leipzig [Leipzig], Semantic Analysis of Natural Language (SEMAGRAMME), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Raffaella Bernardi, Greg Kobele, Sylvain Pogodalla, and Universität Leipzig

Subjects
ComputingMilieux_MISCELLANEOUS, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience

Published
2019
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15. Grounded Textual Entailment

Author

Hoa Trong Vu, Greco, Claudio, Aliia, Erofeeva, Somayeh, Jafaritazehjan, Guido, Linders, Marc, Tanti, Alberto, Testoni, Bernardi, Raffaella, and Albert, Gatt

Published
2018

17. Linguistic issues behind visual question answering.

Author

Bernardi, Raffaella and Pezzelle, Sandro

Subjects
COMPUTATIONAL linguistics, COMPUTER vision, NATURAL languages, SYNTAX (Grammar), MACHINE learning
Abstract

Answering a question that is grounded in an image is a crucial ability that requires understanding the question, the visual context, and their interaction at many linguistic levels: among others, semantics, syntax and pragmatics. As such, visually‐grounded questions have long been of interest to theoretical linguists and cognitive scientists. Moreover, they have inspired the first attempts to computationally model natural language understanding, where pioneering systems were faced with the highly challenging task—still unsolved—of jointly dealing with syntax, semantics and inference whilst understanding a visual context. Boosted by impressive advancements in machine learning, the task of answering visually‐grounded questions has experienced a renewed interest in recent years, to the point of becoming a research sub‐field at the intersection of computational linguistics and computer vision. In this paper, we review current approaches to the problem which encompass the development of datasets, models and frameworks. We conduct our investigation from the perspective of the theoretical linguists; we extract from pioneering computational linguistic work a list of desiderata that we use to review current computational achievements. We acknowledge that impressive progress has been made to reconcile the engineering with the theoretical view. At the same time, we claim that further research is needed to get to a unified approach which jointly encompasses all the underlying linguistic problems. We conclude the paper by sharing our own desiderata for the future. [ABSTRACT FROM AUTHOR]

Published
2021
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18. Proceedings of the Third Italian Conference on Computational Linguistics CLiC-it 2016

Author

Abad, Azad, Abel, Andrea, Alfieri, Linda, Alicante, Anita, Arcara, Giorgio, Baiamonte, Daniela, Barlacchi, Gianni, Basile, Pierpaolo, Basile, Valerio, Basili, Roberto, Bellandi, Andrea, Benjamin, Martin, Benotto, Giulia, Bernardi, Raffaella, Bogers, Toine, Bompolas, Stavros, Bondielli, Alessandro, Bordea, Georgeta, Bosco, Cristina, Bottini, Roberto, Bracchi, Alice, Brunato, Dominique, Budassi, Marco, Buitelaar, Paul, Cabrio, Elena, Caputo, Annalina, Cardillo, Franco Alberto, Caruso, Valeria, Casasanto, Daniel, Caselli, Tommaso, Chatterjee, Rajen, Cherchi, Manuela, Chiusaroli, Francesca, Corazza, Anna, Corino, Elisa, Crepaldi, Davide, Croce, Danilo, Culy, Chris, Curci, Antonietta, Cutugno, Francesco, David, Alfter, Dell’Orletta, Felice, Del Tredici, Marco, Desantis, Anna, De Martino, Maria, De Meo, Anna, Di Nunzio, Giorgio Maria, Esposito, Fabrizio, Fantini, Anna, Feltracco, Anna, Ferro, Marcello, Ferro, Nicola, Filice, Simone, Franzon, Francesca, Frey, Jennifer-Carmen, Gagné, Christina L., Gebremelak, Gebremedhen, Giovannetti, Emiliano, Glaznieks, Aivars, Gregori, Lorenzo, Guglielmi, Francesca, Herbelot, Aurelie, Hernández Farías, Delia Irazú, Iovino, Rossella, Isgrò, Francesco, Jezek, Elisabetta, Lai, Mirko, Laudanna, Alessandro, Lavelli, Alberto, Lebani, Gianluca E., Lenci, Alessandro, Lieto, Antonio, Litta, Eleonora, Logozzo, Felicia, Luisi, Roberta, Maggio, Valerio, Magnini, Bernardo, Maistro, Maria, Mancuso, Azzurra, Mansour, Sina, Marchi, Simone, Marzi, Claudia, Mazzei, Alessandro, Mencarini, Letizia, Mensa, Enrico, Minard, Anne-Lyse, Mitkov, Ruslan, Montemagni, Simonetta, Monti, Johanna, Moretti, Giovanni, Moschitti, Alessandro, Mozzachiodi, Michele, Nadalini, Andrea, Nardi, Daniele, Negri, Matteo, Nicolas, Lionel, Nissim, Malvina, Orletti, Franca, Palmero Aprosio, Alessio, Panunzi, Alessandro, Passaro, Lucia C., Passarotti, Marco, Patti, Viviana, Pezzelle, Sandro, Piccini, Silvia, Pieri, Giulia, Pironti, Antonio, Pirrelli, Vito, Pisano, Simone, Ponti, Edoardo Maria, Prodanof, Irina, Qwaider, Mohammed R. H., Radicioni, Daniele P., Ravelli, Andrea Amelio, Rodda, Martina A., Rossinelli, Emanuele, Russo, Claudio, Russo, Irene, Saltori, Francesca, Sangati, Federico, Scanniello, Giuseppe, Semeraro, Giovanni, Senaldi, Marco S.G., Senaldi, Marco S. G., Silvello, Gianmaria, Silvestri, Stefano, Soria, Claudia, Sorodoc, Ionut, Spalding, Thomas L., Speranza, Manuela, Sprugnoli, Rachele, Stede, Manfred, Stemle, Egon, Stemle, Egon W., Sulis, Emilio, Tamburini, Fabio, Taslimipoor, Shiva, Tonelli, Sara, Turchi, Marco, Tusa, Erica, Uva, Antonio, Vanzo, Andrea, Venturi, Giulia, Vignoli, Daniele, Villata, Serena, Vitale, Vincenzo Norman, Yuri, Bizzoni, Zaninello, Andrea, Zanini, Chiara, Zilio, Daniel, Corazza, Anna, Montemagni, Simonetta, and Semeraro, Giovanni

Subjects
Computational Linguistics, Linguistique Computationelle, LNP, linguaggio naturale, LAW009000, méthodes expérimentales, experimental methodologies, langage naturel, Language & Linguistics (General), metodologie sperimentali, natural language, Linguistica Computazionale
Abstract

The annual conference CLIC–it (''Italian Conference on Computational Linguistics'') is an initiative of the ''Italian Association of Computational Linguistics'' (AILC – www.ai-lc.it) which is intended to meet the need for a national and international forum for the promotion and dissemination of high-level original research in the field of Computational Linguistics (CL), with particular emphasis on Italian. The volume gathers the Proceedings of the ''Third Italian Conference on Computational Linguistics'' (CLiC–it 2016), held in Naples on 5-6 December 2016. The CLiC–it 2016 papers cover a wide range of topics in the area of computational linguistics and natural language (both written and spoken) processing, by targeting state–of–art theoretical results, experimental methodologies, technologies and application perspectives, and by addressing challenges, open issues and new perspectives related to current and novel trends of the discipline.

Published
2017

20. Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures (Extended Abstract)

Author

Bernardi, Raffaella, Cakici, Ruket, Elliott, Desmond, Erdem, Aykut, Erdem, Erkut, Ikizler-Cinbis, Nazli, Keller, Frank, Muscat, Adrian, Plank, Barbara, and Sierra, Carles

Abstract

Automatic image description generation is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the known approaches based on how they conceptualise this problem and provide a review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image-text datasets and the evaluation measures that have been developed to assess the quality of machine-generated descriptions. Finally we explore future directions in the area of automatic image description.

Published
2017

21. Representation of sentence meaning (A JNLE Special Issue).

Author

Bojar, Ondřej, Bernardi, Raffaella, and Webber, Bonnie

Subjects
CRIMINAL sentencing, VECTOR spaces
Abstract

This paper serves as a short overview of the JNLE special issue on representation of the meaning of the sentence, bringing together traditional symbolic and modern continuous approaches. We indicate notable aspects of sentence meaning and their compatibility with the two streams of research and then summarize the papers selected for this special issue. [ABSTRACT FROM AUTHOR]

Published
2019
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22. Hierarchical Classification of OAI Metadata Using the DDC Taxonomy

Author

Waltinger, Ulli, Mehler, Alexander, Lösch, Mathias, Horstmann, Wolfram, Bernardi, Raffaella, Chambers, Sally, Gottfried, Björn, Segond, Frédérique, and Zaihrayeu, Ilya

Subjects
Information retrieval, computer.internet_protocol, Computer science, Subject indexing, SVM, Protocol for Metadata Harvesting, Digital library, Metadata repository, law.invention, OAI-PMH, Bielefeld Academic Search Engine, Metadata, law, Digital Library, Hierarchical Classification, Dewey Decimal Classification, computer, Classifier (UML), XML
Abstract

In the area of digital library services, the access to subject-specific metadata of scholarly publications is of utmost interest. One of the most prevalent approaches for metadata exchange is the XML-based Open Archive Initiative (OAI) Protocol for Metadata Harvesting (OAIPMH). However, due to its loose requirements regarding metadata content there is no strict standard for consistent subject indexing specified, which is furthermore needed in the digital library domain. This contribution addresses the problem of automatic enhancement of OAI metadata by means of the most widely used universal classification schemes in libraries--the Dewey Decimal Classification (DDC). To be more specific, we automatically classify scientific documents according to the DDC taxonomy within three levels using a machine learning-based classifier that relies solely on OAI metadata records as the document representation. The results show an asymmetric distribution of documents across the hierarchical structure of the DDC taxonomy and issues of data sparseness. However, the performance of the classifier shows promising results on all three levels of the DDC.

Published
2011

23. There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics.

Author

Kruszewski, Germán, Paperno, Denis, Bernardi, Raffaella, and Baroni, Marco

Subjects
NEGATION (Logic), SEMANTICS, CONVERSATION, NOMINALISM, PREDICATE (Logic)
Abstract

Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a "negated" distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward logical negation. If someone states, in the middle of a conversation, that "This is not a dog," the negation strongly suggests a restricted set of alternative predicates that might hold true of the object being talked about. In particular, other canids and middle-sized mammals are plausible alternatives, birds are less likely, skyscrapers and other large buildings virtually impossible. Conversational negation acts like a graded similarity function, of the sort that distributional semantics might be good at capturing. In this article, we introduce a large data set of alternative plausibility ratings for conversationally negated nominal predicates, and we show that simple similarity in distributional semantic space provides an excellent fit to subject data. On the one hand, this fills a gap in the literature on conversational negation, proposing distributional semantics as the right tool to make explicit predictions about potential alternatives of negated predicates. On the other hand, the results suggest that negation, when addressed from a broader pragmatic perspective, far from being a nuisance, is an ideal application domain for distributional semantic methods. [ABSTRACT FROM AUTHOR]

Published
2016
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26. Analyzing Interactive QA Dialogues Using Logistic Regression Models.

Author

Kirschner, Manuel, Bernardi, Raffaella, Baroni, Marco, and Dinh, Le Thanh

Abstract

With traditional Question Answering (QA) systems having reached nearly satisfactory performance, an emerging challenge is the development of successful Interactive Question Answering (IQA) systems. Important IQA subtasks are the identification of a dialogue-dependent typology of Follow Up Questions (FU Qs), automatic detection of the identified types, and the development of different context fusion strategies for each type. In this paper, we show how a system relying on shallow cues to similarity between utterances in a narrow dialogue context and other simple information sources, embedded in a machine learning framework, can improve FU Q answering performance by implicitly detecting different FU Q types and learning different context fusion strategies to help re-ranking their candidate answers. [ABSTRACT FROM AUTHOR]

Published
2009
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27. English Querying over Ontologies: E-QuOnto.

Author

Carbonell, Jaime G., Siekmann, Jörg, Basili, Roberto, Pazienza, Maria Teresa, Bernardi, Raffaella, Bonin, Francesca, Calvanese, Diego, Carbotta, Domenico, and Thorne, Camilo

Abstract

Relational database (DB) management systems provide the standard means for structuring and querying large amounts of data. However, to access such data the exact structure of the DB must be know, and such a structure might be far from the conceptualization of a human being of the stored information. Ontologies help to bridge this gap, by providing a high level conceptual view of the information stored in a DB in a cognitively more natural way. Even in this setting, casual end users might not be familiar with the formal languages required to query ontologies. In this paper we address this issue and study the problem of ontology-based data access by means of natural language questions instead of queries expressed in some formal language. Specifically, we analyze how complex real life questions are and how far from the query languages accepted by ontology-based data access systems, how we can obtain the formal query representing a given natural language question, and how can we handle those questions which are too complex wrt the accepted query language. [ABSTRACT FROM AUTHOR]

Published
2007
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28. Continuation Semantics for Symmetric Categorial Grammar.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Leivant, Daniel, de Queiroz, Ruy, Bernardi, Raffaella, and Moortgat, Michael

Abstract

Categorial grammars in the tradition of Lambek [1,2] are asymmetric: sequent statements are of the form ${\Gamma}\Rightarrow{A}$, where the succedent is a single formula A, the antecedent a structured configuration of formulas A1,...,An. The absence of structural context in the succedent makes the analysis of a number of phenomena in natural language semantics problematic. A case in point is scope construal: the different possibilities to build an interpretation for sentences containing generalized quantifiers and related expressions. In this paper, we explore a symmetric version of categorial grammar based on work by Grishin [3]. In addition to the Lambek product, left and right division, we consider a dual family of type-forming operations: coproduct, left and right difference. Communication between the two families is established by means of structure-preserving distributivity principles. We call the resulting system LG. We present a Curry-Howard interpretation for derivations. Our starting point is Curien and Herbelin's sequent system for λμ calculus [4] which capitalizes on the duality between logical implication (i.e. the Lambek divisions under the formulas-as-types perspective) and the difference operation. Importing this system into categorial grammar requires two adaptations: we restrict to the subsystem where linearity conditions are in effect, and we refine the interpretation to take the left-right symmetry and absence of associativity/commutativity into account. We discuss the continuation-passing-style (CPS) translation, comparing the call-by-value and call-by-name evaluation regimes. We show that in the latter (but not in the former) the types of LG are associated with appropriate denotational domains to enable a proper treatment of scope construal. [ABSTRACT FROM AUTHOR]

Published
2007
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29. Generalized Quantifiers in Declarative and Interrogative Sentences.

Author

Bernardi, Raffaella and Moot, Richard

Subjects
QUANTIFIERS (Linguistics), INTERROGATIVE (Grammar), LOGIC, INFERENCE (Logic), SEMANTICS
Abstract

In this paper we present a logical system able to compute the semantics of both declarative and interrogative sentences. Our proposed analysis takes place at both the sentential and at the discourse level. We use syntactic inference on the sentential level for declarative sentences, while the discourse level comes into play for our treatment of questions. Our formalisation uses a type logic sensitive to both the syntactic and semantic properties of natural language. We will show how an account of the linguistic data follows naturally from the logical relations inherent in the type logic. [ABSTRACT FROM PUBLISHER]

Published
2003
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31. Games for Learning Old and Special Alphabets – The Case Study of Gamifying Mrežnik

Author

Mihaljević, Josip, Bernardi, Raffaella, Navigli, Roberto, and Semeraro, Giovanni

Subjects
Braille alphabet, e-learning, games, sign language, ComputingMilieux_PERSONALCOMPUTING
Abstract

This paper presents many different custom made web games which are created for learning the Glagolitic script, the sign language, and the Braille alphabet. These games were created within The Croatian Web Dictionary Project – Mrežnik where the author works on gamifying dictionary content. The games for learning the Glagolitic script, sign language, and Braille alphabet will be connected to the entries glagoljica (the Glagolitic script), brajica (Braille alphabet), and the subentry znakovni jezik (sign language) of the entry jezik (language) in Mrežnik. In the paper, each of these games will be presented by stating the game type, mechanics, and gamification elements such as scoring, leaderboards, levels, and badges, etc. The position of these games in the structure of Mrežnik will be shown and the reception of the published games through Facebook likes and shares will be presented. For Glagolitic games, a statistical analysis will also be given to show how many players have completed the game, submitted their results, and replayed the game. At the end of the paper technology used for creating, testing, and publishing these games will also be analyzed.

Published
2019

32. Moving towards adaptive search in digital libraries

Author

Maria Fasli, Nikolaos Nanas, Anne De Roeck, M-Dyaa Albakour, Jinzhong Niu, Udo Kruschwitz, Johannes Leveling, Dawei Song, Yunhyong Kim, Bernardi, Raffaella, Chambers, Sally, Gottfried, Björn, Segond, Frédérique, and Zaihrayeu, Ilya

Subjects
Information retrieval, Association rule learning, Computer science, Specific-information, 05 social sciences, A domain, 02 engineering and technology, Domain model, Ant colony, Digital library, World Wide Web, Search engine query, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Domain knowledge, 0509 other social sciences, 050904 information & library sciences
Abstract

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour.

Published
2011

33. She adapts to her student: An expert pragmatic speaker tailoring her referring expressions to the Layman listener.

Author

Greco C, Bagade D, Le DT, and Bernardi R

Abstract

Communication is a dynamic process through which interlocutors adapt to each other. In the development of conversational agents, this core aspect has been put aside for several years since the main challenge was to obtain conversational neural models able to produce utterances and dialogues that at least at the surface level are human-like. Now that this milestone has been achieved, the importance of paying attention to the dynamic and adaptive interactive aspects of language has been advocated in several position papers. In this paper, we focus on how a Speaker adapts to an interlocutor with different background knowledge. Our models undergo a pre-training phase, through which they acquire grounded knowledge by learning to describe an image, and an adaptive phase through which a Speaker and a Listener play a repeated reference game. Using a similar setting, previous studies focus on how conversational models create new conventions; we are interested, instead, in studying whether the Speaker learns from the Listener's mistakes to adapt to his background knowledge. We evaluate models based on Rational Speech Act (RSA), a likelihood loss, and a combination of the two. We show that RSA could indeed work as a backbone to drive the Speaker toward the Listener: in the combined model, apart from the improved Listener's accuracy, the language generated by the Speaker features the changes that signal adaptation to the Listener's background knowledge. Specifically, captions to unknown object categories contain more adjectives and less direct reference to the unknown objects., Competing Interests: D-TL was employed by Amazon Alexa. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Greco, Bagade, Le and Bernardi.)

Published
2023
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34. Artificial Intelligence Models Do Not Ground Negation, Humans Do. GuessWhat?! Dialogues as a Case Study.

Author

Testoni A, Greco C, and Bernardi R

Abstract

Negation is widely present in human communication, yet it is largely neglected in the research on conversational agents based on neural network architectures. Cognitive studies show that a supportive visual context makes the processing of negation easier. We take GuessWhat?!, a referential visually grounded guessing game, as test-bed and evaluate to which extent guessers based on pre-trained language models profit from negatively answered polar questions. Moreover, to get a better grasp of models' results, we select a controlled sample of games and run a crowdsourcing experiment with subjects. We evaluate models and humans against the same settings and use the comparison to better interpret the models' results. We show that while humans profit from negatively answered questions to solve the task, models struggle in grounding negation, and some of them barely use it; however, when the language signal is poorly informative, visual features help encoding the negative information. Finally, the experiments with human subjects put us in the position of comparing humans and models' predictions and get a grasp about which models make errors that are more human-like and as such more plausible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Testoni, Greco and Bernardi.)

Published
2022
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