BibTeX
@inproceedings{attardi2009tanl,
title={The Tanl Named Entity Recognizer at Evalita 2009},
author={Attardi, Giuseppe and Dei Rossi, Stefano and Dell'Orletta, Felice and Vecchi, Eva Maria},
booktitle={Evalita 2009},
year={2009}
}
@inproceedings{attardi2009experiments,
title={Experiments in tagger combination: arbitrating, guessing, correcting, suggesting},
author={Attardi, Giuseppe and Fuschetto, Antonio and Tamberi, Francesco and Simi, Maria and Vecchi, Eva Maria},
booktitle={Proc. of Workshop Evalita},
pages={10},
year={2009}
}
@inproceedings{boleda2012first,
title={First order vs. higher order modification in distributional semantics},
author={Boleda, Gemma and Vecchi, Eva Maria and Cornudella, Miquel and McNally, Louise},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={1223-1233},
year={2012}
}
@article{dell2010tecnologie,
title={Tecnologie linguistico-computazionali per il monitoraggio delle competenze linguistiche di apprendenti lâitaliano come L2},
author={DellâOrletta, F and Montemagni, S and Vecchi, EM and Venturi, G},
journal={L2: Italiano lingua seconda nell'Universit{à}, nella Scuola e sul Territorio},
pages={12-13},
year={2010}
}
@inproceedings{falenska-etal-2024-self-reported,
abstract = {Research on language as interactive discourse underscores the deliberate use of demographic parameters such as gender, ethnicity, and class to shape social identities. For example, by explicitly disclosing one{'}s information and enforcing one{'}s social identity to an online community, the reception by and interaction with the said community is impacted, e.g., strengthening one{'}s opinions by depicting the speaker as credible through their experience in the subject. Here, we present a first thorough study of the role and effects of self-disclosures on online discourse dynamics, focusing on a pervasive type of self-disclosure: author gender. Concretely, we investigate the contexts and properties of gender self-disclosures and their impact on interaction dynamics in an online persuasive forum, ChangeMyView. Our contribution is twofold. At the level of the target phenomenon, we fill a research gap in the understanding of the impact of these self-disclosures on the discourse by bringing together features related to forum activity (votes, number of comments), linguistic/stylistic features from the literature, and discourse topics. At the level of the contributed resource, we enrich and release a comprehensive dataset that will provide a further impulse for research on the interplay between gender disclosures, community interaction, and persuasion in online discourse.},
address = {Torino, Italia},
author = {Falenska, Agnieszka and Vecchi, Eva Maria and Lapesa, Gabriella},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
month = {05},
pages = {14606-14621},
publisher = {ELRA and ICCL},
title = {Self-reported Demographics and Discourse Dynamics in a Persuasive Online Forum},
url = {https://aclanthology.org/2024.lrec-main.1272},
year = 2024
}
@InProceedings{falk-etal-2021-predicting,
title = "Predicting Moderation of Deliberative Arguments: Is Argument
Quality the Key?",
author = "Falk, Neele and
Jundi, Iman and
Vecchi, Eva Maria and
Lapesa, Gabriella",
booktitle = "Proceedings of the 8th Workshop on Argument Mining",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.argmining-1.13",
pages = "133-141",
}
@inproceedings{falk-etal-2024-moderation,
title = "Moderation in the Wild: Investigating User-Driven Moderation in Online Discussions",
author = "Falk, Neele and
Vecchi, Eva and
Jundi, Iman and
Lapesa, Gabriella",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.60/",
pages = "992-1013",
abstract = "Effective content moderation is imperative for fostering healthy and productive discussions in online domains. Despite the substantial efforts of moderators, the overwhelming nature of discussion flow can limit their effectiveness. However, it is not only trained moderators who intervene in online discussions to improve their quality. { textquotedblleft }Ordinary{ textquotedblright } users also act as moderators, actively intervening to correct information of other users' posts, enhance arguments, and steer discussions back on course.This paper introduces the phenomenon of user moderation, documenting and releasing UMOD, the first dataset of comments in whichusers act as moderators. UMOD contains 1000 comment-reply pairs from the subreddit r/changemyview with crowdsourced annotations from a large annotator pool and with a fine-grained annotation schema targeting the functions of moderation, stylistic properties(aggressiveness, subjectivity, sentiment), constructiveness, as well as the individual perspectives of the annotators on the task. The releaseof UMOD is complemented by two analyses which focus on the constitutive features of constructiveness in user moderation and on thesources of annotator disagreements, given the high subjectivity of the task."
}
@inproceedings{fagaracsan2015distributional,
title={From distributional semantics to feature norms: grounding semantic models in human perceptual data},
author={F u {a}g u {a}r u {a}{ c {s}}an, Luana and Vecchi, Eva Maria and Clark, Stephen},
booktitle={Proceedings of the 11th international conference on computational semantics},
pages={52-57},
year={2015}
}
@InProceedings{herbelot-vecchi:2015:EMNLP,
author = {Herbelot, Aurélie and Vecchi, Eva Maria},
title = {Building a shared world: mapping distributional to model-theoretic semantic spaces},
booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
month = {September},
year = {2015},
address = {Lisbon, Portugal},
publisher = {Association for Computational Linguistics},
pages = {22-32},
url = {https://www.aclweb.org/anthology/D15-1003}
}
@article{Herbelot:Vecchi:2016,
author = {Herbelot, Aurélie and Vecchi, Eva Maria},
title = {Many speakers, many worlds: {I}nterannotator variations in the quantification of feature norms},
year = 2016,
journal = {Linguistic Issues in Language Technology},
volume = 13,
number=2,
pages={37-57}
}
@inproceedings{jundi-etal-2023-node,
title = "Node Placement in Argument Maps: Modeling Unidirectional Relations in High {&} Low-Resource Scenarios",
author = "Jundi, Iman and
Falk, Neele and
Vecchi, Eva Maria and
Lapesa, Gabriella",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.322/",
doi = "10.18653/v1/2023.acl-long.322",
pages = "5854-5876",
abstract = "Argument maps structure discourse into nodes in a tree with each node being an argument that supports or opposes its parent argument. This format is more comprehensible and less redundant compared to an unstructured one. Exploring those maps and maintaining their structure by placing new arguments under suitable parents is more challenging for users with huge maps that are typical in online discussions. To support those users, we introduce the task of node placement: suggesting candidate nodes as parents for a new contribution. We establish an upper-bound of human performance, and conduct experiments with models of various sizes and training strategies. We experiment with a selection of maps from Kialo, drawn from a heterogeneous set of domains. Based on an annotation study, we highlight the ambiguity of the task that makes it challenging for both humans and models. We examine the unidirectional relation between tree nodes and show that encoding a node into different embeddings for each of the parent and child cases improves performance. We further show the few-shot effectiveness of our approach."
}
@inproceedings{jundi-etal-2025-negative,
title = "It Is Not Only the Negative that Deserves Attention! Understanding, Generation {&} Evaluation of (Positive) Moderation",
author = "Jundi, Iman and
Vecchi, Eva Maria and
Quensel, Carlotta and
Falk, Neele and
Lapesa, Gabriella",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.567/",
pages = "11360-11395",
ISBN = "979-8-89176-189-6",
abstract = "Moderation is essential for maintaining and improving the quality of online discussions. This involves: (1) countering negativity, e.g. hate speech and toxicity, and (2) promoting positive discourse, e.g. broadening the discussion to involve other users and perspectives. While significant efforts have focused on addressing negativity, driven by an urgency to address such issues, this left moderation promoting positive discourse (henceforth PositiveModeration) under-studied. With the recent advancements in LLMs, Positive Moderation can potentially be scaled to vast conversations, fostering more thoughtful discussions and bridging the increasing divide in online interactions.We advance the understanding of Positive Moderation by annotating a dataset on 13 moderation properties, e.g. neutrality, clarity and curiosity. We extract instructions from professional moderation guidelines and use them to prompt LLaMA to generate such moderation. This is followed by extensive evaluation showing that (1) annotators rate generated higher than professional moderation, but still slightly prefer professional moderation in pairwise comparison, and (2) LLMs can be used to estimate human evaluation as an efficient alternative."
}
@inproceedings{lapesa-etal-2023-mining,
title = "Mining, Assessing, and Improving Arguments in {NLP} and the Social Sciences",
author = "Lapesa, Gabriella and
Vecchi, Eva Maria and
Villata, Serena and
Wachsmuth, Henning",
editor = "Zanzotto, Fabio Massimo and
Pradhan, Sameer",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-tutorials.1/",
doi = "10.18653/v1/2023.eacl-tutorials.1",
pages = "1-6",
abstract = "Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention in the community: argument quality assessment, that is, what makes an argument good or bad? We structure the tutorial along three main coordinates: (1) the notions of argument quality across disciplines (how do we recognize good and bad arguments?), (2) the modeling of subjectivity (who argues to whom; what are their beliefs?), and (3) the generation of improved arguments (what makes an argument better?). The tutorial highlights interdisciplinary aspects of the field, ranging from the collaboration of theory and practice (e.g., in NLP and social sciences), to approaching different types of linguistic structures (e.g., social media versus parliamentary texts), and facing the ethical issues involved (e.g., how to build applications for the social good). A key feature of this tutorial is its interactive nature: We will involve the participants in two annotation studies on the assessment and the improvement of quality, and we will encourage them to reflect on the challenges and potential of these tasks."
}
@inproceedings{lapesa-etal-2024-mining,
title = "Mining, Assessing, and Improving Arguments in {NLP} and the Social Sciences",
author = "Lapesa, Gabriella and
Vecchi, Eva Maria and
Villata, Serena and
Wachsmuth, Henning",
editor = "Klinger, Roman and
Okazaki, Naozaki and
Calzolari, Nicoletta and
Kan, Min-Yen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-tutorials.5/",
pages = "26-32",
abstract = "Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. The focus of recent research has concentrated on textit {argument quality assessment}: what makes an argument good or bad? We present a tutorial which is an updated edition of the EACL 2023 tutorial presented by the same authors. As in the previous version, the tutorial will have a strong interdisciplinary and interactive nature, and will be structured along three main coordinates: (1) the notions of argument quality (AQ) across disciplines (how do we recognize good and bad arguments?), with a particular focus on the interface between Argument Mining (AM) and Deliberation Theory; (2) the modeling of subjectivity (who argues to whom; what are their beliefs?); and (3) the generation of improved arguments (what makes an argument better?). The tutorial will also touch upon a series of topics that are particularly relevant for the LREC-COLING audience (the issue of resource quality for the assessment of AQ; the interdisciplinary application of AM and AQ in a text-as-data approach to Political Science), in line with the developments in NLP (LLMs for AQ assessment), and relevant for the societal applications of AQ assessment (bias and debiasing). We will involve the participants in two annotation studies on the assessment and the improvement of quality."
}
@inproceedings{lazaridou2013fish,
title={Fish transporters and miracle homes: How compositional distributional semantics can help NP parsing},
author={Lazaridou, Angeliki and Vecchi, Eva Maria and Baroni, Marco},
booktitle={Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
pages={1908-1913},
year={2013}
}
@inproceedings{miller2008infrastructure,
title={An Infrastructure, Tools and Methodology for Evaluation of Multicultural Name Matching Systems},
author={Miller, Keith J and Arehart, Mark and Ball, Catherine N and Polk, John and Rubenstein, Alan and Samuel, Ken and Schroeder, Elizabeth and Vecchi, Eva Maria and Wolf, Chris},
booktitle={Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
year={2008}
}
@inproceedings{vecchi2011linear,
title={(Linear) maps of the impossible: capturing semantic anomalies in distributional space},
author={Vecchi, Eva Maria and Baroni, Marco and Zamparelli, Roberto},
booktitle={Proceedings of the Workshop on Distributional Semantics and Compositionality},
pages={1-9},
year={2011}
}
@InProceedings{vecchi-etal-2021-towards,
title = "Towards Argument Mining for Social Good: A Survey",
author = "Vecchi, Eva Maria and
Falk, Neele and
Jundi, Iman and
Lapesa, Gabriella",
booktitle = "Proceedings of the 59th Annual Meeting of the Association
for Computational Linguistics and the 11th International Joint Conference
on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.107",
doi = "10.18653/v1/2021.acl-long.107",
pages = "1338-1352",
}
@article{Vecchi:etal:2017,
author = {Eva Maria Vecchi and Marco Marelli and Roberto Zamparelli and Marco Baroni},
title = {Spicy adjectives and nominal donkeys: {C}apturing semantic deviance using compositionality in distributional spaces},
journal = {Cognitive Science},
volume={41},
number={1},
pages={102-136},
year={2017},
publisher={Wiley Online Library},
doi = {10.1111/cogs.12330}
}
@inproceedings{vecchi2013studying,
title={Studying the recursive behaviour of adjectival modification with compositional distributional semantics},
author={Vecchi, Eva Maria and Zamparelli, Roberto and Baroni, Marco},
booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
pages={141-151},
year={2013}
}
@inproceedings{venturi2009towards,
title={Towards a FrameNet resource for the legal domain},
author={Venturi, Giulia and Lenci, Alessandro and Montemagni, Simonetta and Vecchi, Eva Maria and Sagri, Maria Teresa and Tiscornia, Daniela and Agnoloni, Tommaso},
booktitle={Proceedings of the 3rd Workshop on Legal Ontologies and Artificial Intelligence Techniques: 2nd Workshop on Semantic Processing of Legal Text},
pages={67-76},
year={2009}
}
@inproceedings{wachsmuth-etal-2024-argument,
title = "Argument Quality Assessment in the Age of Instruction-Following Large Language Models",
author = "Wachsmuth, Henning and
Lapesa, Gabriella and
Cabrio, Elena and
Lauscher, Anne and
Park, Joonsuk and
Vecchi, Eva Maria and
Villata, Serena and
Ziegenbein, Timon",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.135/",
pages = "1519-1538",
abstract = "The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argument{'}s quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby."
}