Papers by Marco Del Tredici
The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities (C18-1)
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| Challenge: | a longitudinal study of online social networks investigates the birth and spread of lexical innovations. |
| Approach: | They investigate the birth and diffusion of lexical innovations in online communities . they build on sociolinguistic theories and focus on the relationship between the spread of a new term and the social role of the individuals who use it . |
| Outcome: | The proposed method predicts whether an innovation will succeed in a community. |
Short-Term Meaning Shift: A Distributional Exploration (N19-1)
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| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
| Outcome: | The proposed model has problems distinguishing meaning shift from referential phenomena, and measures contextual variability to remedy this. |
Words are the Window to the Soul: Language-based User Representations for Fake News Detection (2020.coling-main)
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| Challenge: | Existing studies on fake news classification focus on textual content, but also social context in which news are consumed. |
| Approach: | They propose a model that creates representations of individuals on social media based only on the language they produce and uses them to detect fake news. |
| Outcome: | The proposed model exploits the relationship between language use and connections in the social graph to assess the presence of the Echo Chamber effect in the data. |
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP (D19-1)
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| Challenge: | Current approaches to social media modelling ignore the fact that an individual may be part of several communities which are not equally relevant in all communicative situations. |
| Approach: | They propose a model that captures the sociological phenomenon of homophily and combines it with linguistic information to make a prediction. |
| Outcome: | The proposed model significantly outperforms existing models on three different tasks and is compared with other models. |
FocusQA: Open-Domain Question Answering with a Context in Focus (2022.findings-emnlp)
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Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert
| Challenge: | a new method for question answering with a context in focus simulates a free interaction with QA systems. |
| Approach: | They introduce question answering with a cotext in focus task that simulates a free interaction with QA systems. |
| Outcome: | The proposed model outperforms state-of-the-art models for question answering with a context in focus up to 21.3% absolute points. |
From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2025.acl-long)
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Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee, Sandro Pezzelle, Marco Del Tredici
| Challenge: | Large Language Models excel at solving individual problems in isolation, but are they able to effectively collaborate over long-term interactions? |
| Approach: | They propose to use a multi-session dataset to test LLMs' ability to track and execute simple coding instructions amid irrelevant information, simulating a realistic setting. |
| Outcome: | The proposed model performs poorly when instructions are spread across sessions, suggesting that they are not able to integrate information over long interactions. |
Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |
Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
| Approach: | They propose a method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. |
| Outcome: | The proposed method captures a variety of synchronic and diachronic linguistic phenomena and is highly reproducible and reproducible. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
| Approach: | They propose to extend benchmark models on a common state-of-the-art evaluation task . they also demonstrate that the same evaluation task and modelling approaches can be utilised for synchronic detection of domain-specific sense divergences in the field of term extraction. |
| Outcome: | The proposed model can be utilised for the detection of domain-specific sense divergences in the field of term extraction. |