Papers by Simone Paolo Ponzetto
Steering Language Models in Multi-Token Generation: A Case Study on Tense and Aspect (2025.emnlp-main)
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| Challenge: | Prior work has focused largely on binary grammatical contrasts, but how do they encode their syntactic knowledge internally? |
| Approach: | They propose to use a multidimensional hierarchical grammar phenomenon to identify distinct, orthogonal directions in residual space to demonstrate causal control over both grammatical features. |
| Outcome: | The proposed model can encode tense and aspect in human-like ways, but effective steering during generation is sensitive to multiple factors and requires manual tuning or automated optimization. |
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers (2023.findings-eacl)
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| Challenge: | Existing studies show that incorporating demographic factors in language representations improves performance on downstream NLP tasks. |
| Approach: | They use continuous language modeling and dynamic multi-task learning to adapt pre-trained Transformers to incorporate demographic information into their representations. |
| Outcome: | The proposed model shows that the results are consistent with previous studies. |
Our kind of people? Detecting populist references in political debates (2023.findings-eacl)
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| Challenge: | Existing literature on populism has only limited agreement on its exact properties . |
| Approach: | They propose a cross-lingual dataset to identify populist rhetoric in text . they propose 'hierarchical' annotation procedure to annotate populist references . |
| Outcome: | The proposed dataset can be used to investigate how political actors talk about The Elite and The People and to study how populist rhetoric is used as a strategic device. |
Out of the Mouths of MPs: Speaker Attribution in Parliamentary Debates (2024.lrec-main)
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| Challenge: | Identifying who says what to whom is an essential prerequisite for analysing human communication. |
| Approach: | They propose a new corpus for speaker attribution in german parliamentary debates . the data includes more than 7,700 manually annotated events of speech, thought and writing . they then apply their model to predict speech events in 20 years of debates and investigate the use of factives in the rhetoric of MPs. |
| Outcome: | The proposed model predicts speech events in 20 years of debates and investigates the use of factives in the rhetoric of MPs. |
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval (D19-3)
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| Challenge: | Existing semantic text encoding models are limited in coverage and few attempts to empirically compare them on IR tasks have been made. |
| Approach: | They propose to implement word embedding aggregators and pretrained semantic encoders and to allow for their comparative evaluation on arbitrary IR collections. |
| Outcome: | The proposed model can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models. |
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)
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| Challenge: | In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions. |
| Approach: | They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles. |
BABELEDITS: A Benchmark and a Modular Approach for Robust Cross-lingual Knowledge Editing of Large Language Models (2025.findings-acl)
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| Challenge: | Existing methods for cross-lingual knowledge editing are limited in their effectiveness and robustness. |
| Approach: | They propose a new CKE benchmark that accounts for the rich variety of entity aliases within and across languages. |
| Outcome: | The proposed method is more effective than state-of-the-art methods and robust against model collapse when subjected to multiple edits. |
Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used for creative tasks such as literary translation. |
| Approach: | They propose a paired-task framework that assesses translational creativity using Units of Creative Potential (UCPs) they benchmark 23 models and four creativity-oriented prompts to assess translational comprehension . |
| Outcome: | The proposed framework compares 23 models and four creativity-oriented prompts on literary excerpts from 11 books. |
Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks (2025.emnlp-main)
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| Challenge: | Existing studies on text embeddings focus less on how information is encoded. |
| Approach: | They find that truncating embedding dimensions causes an increase in performance when removed. |
| Outcome: | The proposed method improves performance across 6 state-of-the-art text encoders and 26 downstream tasks. |
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models (D18-1)
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| Challenge: | Scientific publications are argumentative and often adhere to well-trodden rhetorical patterns and argumentation schemes. |
| Approach: | They investigate the link between scientific publications and rhetorical aspects such as discourse categories or citation contexts by coupling rhetorical classifiers with extraction of argumentative components. |
| Outcome: | The proposed models show significant performance gains for different rhetorical analysis tasks. |
How to Do Politics with Words: Investigating Speech Acts in Parliamentary Debates (2024.lrec-main)
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| Challenge: | a new perspective on framing through the lens of speech acts investigates how politicians make use of different pragmatic speech act functions in political debates. |
| Approach: | They propose a new framework for framing through the lens of speech acts and an annotation scheme for political debates. |
| Outcome: | The proposed framework can predict speech acts with an avg. F1 of around 82.0% . the proposed framework is based on a dataset of German parliamentary debates . |
CATS: A Tool for Customized Alignment of Text Simplification Corpora (L18-1)
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| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)
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Ines Rehbein, Simone Paolo Ponzetto, Anna Adendorf, Oke Bahnsen, Lukas Stoetzer, Heiner Stuckenschmidt
| Challenge: | In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks. |
| Approach: | They propose a task of recognizing from news coverage the (un)willingness of political parties to form a coalition from text and a sub-task of predicting the polarity of the signal. |
| Outcome: | The proposed approach improves over a strong monolingual transfer learning baseline. |
Massively Multilingual Lexical Specialization of Multilingual Transformers (2023.acl-long)
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| Challenge: | Existing work focused on lexical specialization of monolingual PLMs with immense quantities of monolinguistic constraints, but recent work shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexicals. |
| Approach: | They propose to expose massively multilingual transformers to multilingual lexical knowledge at scale using BabelNet as a source of multilingual and cross-lingual type-level lexicon knowledge. |
| Outcome: | The proposed method shows that pretrained language models can be rewired to produce high-quality word representations and perform type-level lexical tasks. |
GenGO Ultra: an LLM-powered ACL Paper Explorer (2025.acl-demo)
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| Challenge: | The main repository of natural language processing (NLP) has grown its number of stored papers by 70% from 2019 to 2023. |
| Approach: | They propose an extension to GenGO Ultra which exploits large language models to dynamically generate responses grounded by published papers. |
| Outcome: | The proposed system exploits large language models to generate responses grounded by published papers and performs multi-granularity experiments. |
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities (P19-4)
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| Challenge: | Political scientists have developed and adopted natural language processing (NLP) methods to exploit text as an additional source of data in their analyses. |
| Approach: | This tutorial aims to provide a gentle introduction to methods and tasks related to computational analysis of political texts from both communities. |
| Outcome: | The main goal of this tutorial is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary area. |
The Robotic Surgery Procedural Framebank (2022.lrec-1)
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| Challenge: | Surgical practice has steadily improved thanks to the support of the approaches made available by observational science. |
| Approach: | They propose to extract from robot-surgical texts verbs and nouns that describe surgical actions and extend PropBank frames by adding any of new lemmas, frames or role sets required to cover missing lemae. |
| Outcome: | The proposed resource can be used to train and evaluate Semantic Role Labeling (SRL) systems in a fine-grained domain setting. |
Enriching Frame Representations with Distributionally Induced Senses (L18-1)
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| Challenge: | lexical resource that enriches Framester knowledge graph with semantic features from text corpora . paves way for development of novel, deeper semantic-aware applications . |
| Approach: | They propose a lexical resource that enriches the Framester knowledge graph with semantic features from text corpora. |
| Outcome: | The proposed resource enables the development of deeper semantic-aware applications . it combines knowledge from text and symbolic representations of events and participants . |
MIsA: Multilingual “IsA” Extraction from Corpora (L18-1)
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| Challenge: | In this paper, we present a collection of hypernymy relations extracted from the Wikipedia corpus in five languages. |
| Approach: | They present a collection of hypernymy relations extracted from the Wikipedia corpus in five languages . they use existing or newly defined lexico-syntactic patterns to extract hyperniyms . |
| Outcome: | The proposed tool is based on a dictionary extracted from the full Wikipedia corpus. |
DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces (2021.eacl-demos)
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| Challenge: | Recent research has shown that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism. |
| Approach: | They propose a platform that measures and mitigates bias in word embeddings by executing two (mutually composable) debiasing models. |
| Outcome: | The proposed platform can measure and mitiga bias in word embeddings. |
Moral Framing in Politics (MFiP): A new resource and models for moral framing (2025.emnlp-main)
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| Challenge: | Recent studies have focused on detecting moral values in political communication, trying to identify moral frames used by political actors or parties to convey their messages. |
| Approach: | They propose to code German parliamentary debates to identify moral framing and to detect subtle differences in politicians’ moral framming. |
| Outcome: | The proposed model distinguishes between different types of moral frames and includes narrative roles, together with the moral foundations for each frame. |
Unsupervised Semantic Frame Induction using Triclustering (P18-2)
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| Challenge: | Recent work on frame-semantics has enabled the development of wide-coverage frame parsers using supervised learning. |
| Approach: | They propose to use dependency triples to perform unsupervised frame induction on a Web-scale corpus. |
| Outcome: | The proposed approach performs state-of-the-art on a FrameNet-derived dataset and performs on par with competitive methods on . verb class clustering task. |
Multilingual and Cross-Lingual Graded Lexical Entailment (P19-1)
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| Challenge: | a novel method for capturing graded (and binary) LE is developed for cross-lingual generalisation of lexical entailment . lexicale enlargement is a key principle behind hierarchical structure found in semantic networks . |
| Approach: | They propose a method for cross-lingual generalisation of GR-LE relation using hyperlex and a bilingual dictionary. |
| Outcome: | The proposed method outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin. |
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages (L18-1)
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Dmitry Ustalov, Denis Teslenko, Alexander Panchenko, Mikhail Chernoskutov, Chris Biemann, Simone Paolo Ponzetto
| Challenge: | Existing systems for word sense disambiguation are limited to the Russian language and lack of resources to address the problem. |
| Approach: | They propose an unsupervised system for word sense disambiguation that uses a traditional vector space model to estimate the most similar word sense corresponding to its context. |
| Outcome: | The proposed system outperforms the sparse mode on all datasets according to the adjusted Rand index. |
Word Sense Disambiguation for 158 Languages using Word Embeddings Only (2020.lrec-1)
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Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko
| Challenge: | Existing methods of disambiguation of word senses are based on knowledge bases, taxonomies, and other externally built resources. |
| Approach: | They propose a method that takes a pre-trained word embedding model and induces a fully-fledged word sense inventory for 158 languages. |
| Outcome: | The proposed model is based on a pre-trained word embedding model and induces a fully-fledged word sense inventory in 158 languages. |