Papers with Italian
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
Thesis Proposal: LLMs post-training for multilingual medical tasks. Instruction-Tuning, Continual-Pretraining or Reasoning? (2026.acl-srw)
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| Challenge: | Adapting Large Language Models to the medical domain remains an active area of research . |
| Approach: | They propose to compare three common adaptation approaches to adapt large language models to the medical domain. |
| Outcome: | The proposed models are built on top of foundational LLMs and rely on different post-training methodologies for domain and task performance. |
The Gutenberg Dialogue Dataset (2021.eacl-main)
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| Challenge: | Current open-domain dialogue datasets offer a trade-off between quality and size . we build a dataset of 14.8M utterances in English and smaller datasets in german, Dutch, Spanish, Portuguese, Italian, and Hungarian . |
| Approach: | They build a high-quality dialogue corpus of 14.8M utterances in English using public-domain books from Project Gutenberg. |
| Outcome: | The proposed datasets show that the extracted dialogues are more accurate and more accurate than the larger Opensubtitles dataset. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
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Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
A Checkpoint on Multilingual Misogyny Identification (2022.acl-srw)
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| Challenge: | a study on hate speech against minorities in Italian tweets found that 1 women are the most targeted group. |
| Approach: | They propose to train monolingual transformers and multilingual transformer models with monolingual data in English, Italian, and Spanish to detect misogyny in tweets. |
| Outcome: | The proposed model achieves state-of-the-art on English, Italian, and Spanish. |
PartisanLens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in European Media (2026.eacl-long)
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Michele Joshua Maggini, Paloma Piot, Anxo Pérez, Erik Bran Marino, Lúa Santamaría Montesinos, Ana Lisboa Cotovio, Marta Vázquez Abuín, Javier Parapar, Pablo Gamallo
| Challenge: | Existing methods for detecting hyperpartisan narratives and PRCTs are limited . hyperpartisan content promotes extreme views through one-sided, emotional language . |
| Approach: | They propose a multilingual dataset of 1617 hyperpartisan news headlines in Spanish, Italian, and Portuguese annotated in multiple political discourse aspects. |
| Outcome: | The proposed dataset is the first multilingual dataset of 1617 hyperpartisan headlines in Spanish, Italian, and Portuguese. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning (2021.acl-long)
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| Challenge: | Existing work has shown that non-linguistic biases in language models obscure linguistic knowledge. |
| Approach: | They hypothesize competing linguistic processes within a language could obscure linguistic knowledge. |
| Outcome: | The proposed model can learn linguistic constraints in a language and their relative ranking, the authors show . linguistic biases can obscure underlying linguistic knowledge, they show a single phenomenon in four languages. |
Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer (2021.emnlp-main)
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| Challenge: | a lack of standardized and reliable methods for automatic evaluation hinders ST . prior work has employed as many as nine different automatic systems to rate formality alone . |
| Approach: | They evaluate automatic metrics on the oft-researched task of formality style transfer . they outline best practices for automatic evaluation in (formality) style transfer and identify models that correlate well with human judgments. |
| Outcome: | The proposed models correlate well with human judgments and are robust across languages. |
Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection (2021.acl-short)
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| Challenge: | a lack of labeled, non-English resources for hate speech detection limits research on hate speech . a recent study shows that zero-shot, cross-lingual learning models cannot be used as they are . lack of consistency limits research, and lack of models for non-english languages limits learning . |
| Approach: | They propose a zero-shot, cross-lingual transfer learning framework for hate speech detection . they use benchmark data sets in English, Italian, and Spanish to detect hate speech . |
| Outcome: | The proposed framework can't be used as it is, but needs to be carefully designed, the authors say . they find that non-hateful, language-specific taboo interjections are misinterpreted as signals of hate speech . |
VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing (2025.emnlp-main)
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Zhisheng Zheng, Puyuan Peng, Anuj Diwan, Cong Phuoc Huynh, Xiaohang Sun, Zhu Liu, Vimal Bhat, David Harwath
| Challenge: | Autoregressive language model for multilingual speech editing and zero-shot text-to-speech synthesis is available in 11 languages. |
| Approach: | They introduce an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot text-to-speech synthesis across 11 languages. |
| Outcome: | The model generates high-quality, natural-sounding speech, even with limited per-language data . it shows robust performance in diverse linguistic settings, even in limited per language data compared to other models . |
Machine Translation for Machines: the Sentiment Classification Use Case (D19-1)
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| Challenge: | Traditionally, machine translation (MT) pursues a "human-oriented" objective: generating fluent output for a downstream task. |
| Approach: | They propose a neural machine translation approach that uses weak feedback to generate translations that are best suited for a downstream task. |
| Outcome: | The proposed approach outperforms general-purpose models and reinforcement learning methods on German and Italian tweets. |
Detecting de minimis Code-Switching in Historical German Books (2020.coling-main)
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| Challenge: | Code-switching has drawn scholarly attention in computational linguistics and natural language processing from many different perspectives. |
| Approach: | They propose to compare informal code-switching to its appearance in more formal registers by annotating and inspecting the German textarchives. |
| Outcome: | The proposed classifiers can help reduce errors when speech recognition is applied to a large corpus with rare embedded languages. |
Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (2020.lrec-1)
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| Challenge: | Existing work on document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. |
| Approach: | They assemble and publish a multilingual Twitter corpus for the task of hate speech detection using inferred author demographic factors. |
| Outcome: | The results show that the classifiers learn human biases and can be discriminatory towards certain demographic groups. |
Multilingual AMR Parsing with Noisy Knowledge Distillation (2021.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph. |
| Approach: | They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation . |
| Outcome: | The proposed model outperforms the current state-of-the-art English-only parser on four different languages. |
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding (2022.findings-acl)
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| Challenge: | Existing research on multimodal pre-training for visually rich document understanding tasks has focused on the English domain while neglecting the importance of multilingual generalization. |
| Approach: | They propose a multimodal pre-trained model for multilingual document understanding which bridges the language barriers for visually rich document understanding. |
| Outcome: | The proposed model outperforms existing cross-lingual pre-trained models on the XFUND dataset on visual document understanding tasks. |
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer (2021.naacl-main)
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| Challenge: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . most work on style transfer within English, while covering different languages has received disproportional interest. |
| Approach: | They create a benchmark of multiple formal reformulations of informal text in Brazil, Brazil, and Italy. |
| Outcome: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . results show that state-of-the-art approaches perform close to simple baselines . |
Beyond Names: How Grammatical Gender Markers Bias LLM-based Educational Recommendations (2026.eacl-long)
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| Challenge: | grammatical gender cues alone trigger substantial distributional shifts in educational recommendations . authors show that up to 76% of the bias exhibited when using prompts with proper names is already present with grammatical gender markers alone. |
| Approach: | They investigate gender biases exhibited by LLM-based virtual assistants in Italian . they show that simply changing noun and adjective endings significantly shifts recommendations . |
| Outcome: | The findings highlight the need for robust bias evaluation and mitigation strategies before deploying LLM-based virtual assistants in student-facing contexts. |
PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies (L18-1)
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Manuela Sanguinetti, Cristina Bosco, Alberto Lavelli, Alessandro Mazzei, Oronzo Antonelli, Fabio Tamburini
| Challenge: | Various approaches and ad hoc resources are needed to provide proper coverage of specific linguistic phenomena. |
| Approach: | They propose to annotate tweets using a well-known dependency-based annotation format . they propose to use the tweets for training NLP systems to improve their performance . |
| Outcome: | The proposed resource can be used for training of NLP systems on social media texts. |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
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| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |
Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset (2024.lrec-main)
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| Challenge: | Existing datasets that include idioms from English, German, Italian, Portuguese and Russian do not include a comprehensive representation of idiomatic expressions in Croatian. |
| Approach: | They propose to extend existing RDF-based multilingual representation of idioms to include 1,042 Croatian idiomes in an Ontolex Lemon format. |
| Outcome: | The proposed resource includes 1,042 Croatian idioms in an Ontolex Lemon format to foster translation initiatives and facilitate intercultural exchange. |
Neural Transduction for Multilingual Lexical Translation (2020.coling-main)
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| Challenge: | a method for completing multilingual translation dictionaries is proposed . a 27% relative improvement in whole-word accuracy is achieved when multilingual data is unavailable . |
| Approach: | They propose a method for completing multilingual translation dictionaries using multilingual inputs and multilingual decoding objective. |
| Outcome: | The proposed method can synthesize new word forms in multilingual translation dictionaries . it can perform in settings where correct translations have not been observed in text . |
LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
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| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis (2020.coling-main)
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| Challenge: | Existing methods to learn general language representations from large volumes of unlabeled text have been used to improve multilingual NLP. |
| Approach: | They propose to use a spatial arrangement method to generate large-scale evaluation datasets that balance cross-lingual alignment with language specificity. |
| Outcome: | The proposed method produces semantic verb classes and fine-grained similarity scores for nearly 130 thousand verb pairs. |
A Large Multilingual and Multi-domain Dataset for Recommender Systems (L18-1)
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| Challenge: | Existing algorithms for recommending items are limited and focused on specific domains. |
| Approach: | They propose a multi-domain interests dataset to train and test Recommender Systems . the english dataset includes an average of 90 preferences per user on music, books, movies, celebrities, sport, politics . |
| Outcome: | The proposed method exploits popular services such as Spotify, Goodreads and others to extract preferences from Twitter messages in Italian and English. |
The Invalsi Benchmarks: measuring the Linguistic and Mathematical understanding of Large Language Models in Italian (2025.coling-main)
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| Challenge: | Invalsi MATE is a high-resource language, but there are few benchmarks to evaluate generative Large Language Models in this language. |
| Approach: | They propose three benchmarks to evaluate language models on mathematical understanding in italian . they use the Invalsi tests, which are administered to students aged 6 to 18 in the italian school system . |
| Outcome: | The proposed benchmarks are based on the Invalsi tests and the Italian highschool math Olympics. |
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages (2020.lrec-1)
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| Challenge: | Sentiment lexica are vital for sentiment analysis in absence of document-level annotations . linguistic resources are limited for at least a few hundred languages, putting them at risk of extinction . |
| Approach: | They introduce UniSent universal sentiment lexica for 1000+ languages . they use a Bible corpus to project sentiment information from English to other languages based on Twitter data . |
| Outcome: | The proposed method mitigates domain mismatch between Bible and Twitter by using embeddings . it compares to other sentiment seeding methods in a subset of languages with ground truth available . |
Datasets for Multilingual Answer Sentence Selection (2024.findings-emnlp)
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| Challenge: | Existing datasets for Answer Sentence Selection (AS2) focus on English due to the scarcity of annotated datasets. |
| Approach: | They propose to use a large language model to train answer sentences in English . they use annotated datasets from English and other languages to train AS2 models . |
| Outcome: | The proposed datasets are highly performant and close the performance gap between English and other languages. |
Denoising Large-Scale Image Captioning from Alt-text Data Using Content Selection Models (2022.coling-1)
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| Challenge: | Recent approaches to training large-scale image captioning (IC) models often fall short in terms of performance in leveraging noisy datasets in favor of clean annotations. |
| Approach: | They propose a technique that breaks down the task into two smaller, more controllable tasks - skeleton prediction and skelet-based caption generation. |
| Outcome: | The proposed method can generate better and denoised captions when using noisy datasets. |
Building Named Entity Recognition Taggers via Parallel Corpora (L18-1)
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| Challenge: | Existing methods to generate semantic processors for languages lacking hand curated data are inefficiently slow and unaffordable in terms of human resources and economic costs. |
| Approach: | They propose to use statistical word alignments to project annotations from multiple sources to a target language. |
| Outcome: | The proposed method is effective to transport NER annotations across languages . it can generate a good statistical model for a new target language . |
Recovering document annotations for sentence-level bitext (2024.findings-acl)
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| Challenge: | In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. |
| Approach: | They propose a document-level filtering technique that discards document- level metadata. |
| Outcome: | The proposed method improves translation without degradation of sentence-level translation. |
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020.lrec-1)
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| Challenge: | Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity. |
| Approach: | They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features. |
| Outcome: | The proposed scheme highlights the tokens responsible for irony activation and their morpho-syntactic features. |
REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays (2020.lrec-1)
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| Challenge: | a new paper aims to reproduce the work described in Vajjala & Rama (2018) . the paper focuses on features-based and neural approaches to essay scoring in Czech, German and Italian . |
| Approach: | They propose to replicate the work described in Vajjala & Rama 2018, ‘Experiments with universal CEFR classification’, as part of REPROLANG 2020. |
| Outcome: | The proposed methods perform better than feature-based models for large text datasets, though neural network modifications do bring performance closer to the best feature-driven models. |
Exploring Paraphrasing Strategies for CEFR A1-Level Constraints in LLMs (2025.findings-emnlp)
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| Challenge: | a new study compares prompt engineering approaches to rephrase general-domain texts . it compares 4 approaches to meet CEFR A1-level constraints in english and italian . |
| Approach: | They compare prompt engineering approaches to rephrase general-domain texts to meet CEFR A1-level constraints in English and Italian. |
| Outcome: | The proposed approaches meet CEFR A1-level constraints in English and Italian. |
Unlocking Legal Knowledge: A Multilingual Dataset for Judicial Summarization in Switzerland (2025.findings-emnlp)
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Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Sarah Hostettler, Lara Burkhalter, Matthias Stürmer, Joel Niklaus
| Challenge: | a dataset of 20K rulings from the Swiss Federal Supreme Court is lacking in legal headnotes due to the high cost of manual annotation. |
| Approach: | They propose a dataset that contains 20K rulings from the Swiss Federal Supreme Court . they fine-tune open models and compare them to larger general-purpose and reasoning-tunned LLMs . |
| Outcome: | The proposed dataset contains 20K rulings from the Swiss Federal Supreme Court with headnotes in German, French, and Italian. |
MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation (2025.findings-emnlp)
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Jackson Trager, Francielle Vargas, Diego Alves, Matteo Guida, Mikel K. Ngueajio, Ameeta Agrawal, Yalda Daryani, Farzan Karimi Malekabadi, Flor Miriam Plaza-del-Arco
| Challenge: | Existing evaluation benchmarks for large language models lack annotations that justify moral classifications and focus on English constrain moral reasoning across diverse cultural settings. |
| Approach: | They propose a multilingual benchmark dataset for evaluating moral reasoning of large language models . it includes 3,000 tweets annotated with binary hate speech labels, moral categories and rationales . |
| Outcome: | The proposed dataset shows a misalignment between LLM outputs and human annotations in moral reasoning tasks. |
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool (2020.lrec-1)
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| Challenge: | Linguistic complexity is a core construct in Second Language Acquisition (SLA) research. |
| Approach: | They present an open source linguistic complexity measurement tool for Italian . they compare it to existing tools for English and germany . |
| Outcome: | The proposed tool is the most comprehensive linguistic complexity measurement tool for italian . it can be used to compare italian texts to multiple other languages in one tool . |
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos (2025.findings-emnlp)
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| Challenge: | a new multimodal dataset of stand-up comedies is proposed to improve humor detection . the dataset is the biggest available for this type of task, and the most diverse . |
| Approach: | They propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. |
| Outcome: | The proposed method improves existing models of humor detection by using audio speech recognition errors. |
Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It) (2024.lrec-main)
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| Challenge: | a recent study has shown that pre-trained NLMs can capture syntax- and semantic-sensitive phenomena. |
| Approach: | They investigate whether fine-tuning pre-trained models with linguistic knowledge improves their performance in a target task. |
| Outcome: | The proposed enhancements improve models' performance in a target task, the authors show . the study includes models in Italian and English, and multilingual models in English and Italian . |
Deep Temporal Reasoning in Video Language Models: A Cross-Linguistic Evaluation of Action Duration and Completion through Perfect Times (2025.acl-long)
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| Challenge: | Experimental results show that video-language models struggle to mirror human-like temporal reasoning grounded in video . Sequential events are not simply arranged chronologically; rather, one event triggers the next upon reaching its completion. |
| Approach: | They propose a quadrilingual dataset to assess temporal reasoning in video-language models . they pair everyday activity videos with event completion labels and perfectivity distractors . |
| Outcome: | The perfect times dataset examines whether video-language models comprehend temporal dynamics . it combines everyday activity videos with event completion labels and perfectivity distractors . results show that state-of-the-art models struggle to mirror human-like temporal reasoning . |
Improving Occupational ISCO Classification of Multilingual Swiss Job Postings with LLM-Refined Training Data (2025.findings-acl)
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| Challenge: | 80% of job postings are German, 11% French, 8% English, and under 1% Italian. |
| Approach: | They propose a method that refines silver-standard ISCO labels by consolidating them with predictions from pre-fine-tuned models to resolve discrepancies. |
| Outcome: | The proposed method raises Top-1 accuracy on silver data to 58.3% and reaches 80% precision on held-out data. |
Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data (2025.findings-emnlp)
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| Challenge: | Maltese is a Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. |
| Approach: | They investigate whether Arabic-language resources can support Maltese natural language processing . they introduce transliteration schemes and machine translation approaches to align Arabic text with Maltesen . |
| Outcome: | The proposed techniques can significantly improve Maltese natural language processing tasks. |
Resolving Legalese: A Multilingual Exploration of Negation Scope Resolution in Legal Documents (2024.lrec-main)
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| Challenge: | Negation scope resolution is a challenging task for NLP because of the complexity of legal texts and lack of annotated in-domain negation corpora. |
| Approach: | They propose to use annotated court decisions to improve negation scope resolution . they release annotations in german, french, and italian to train models without legal data . |
| Outcome: | The proposed models achieve token-level F1-scores of up to 86.7% in zero-shot and multilingual settings. |
SwissSLi: The Multi-parallel Sign Language Corpus for Switzerland (2024.lrec-main)
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| Challenge: | Using a CC BY-NC-SA 4.0 license, this corpus contains parallel sign language videos and spoken language subtitles. |
| Approach: | They introduce SwissSLi, the first sign language corpus that contains parallel data of all three Swiss sign languages. |
| Outcome: | The proposed corpus contains parallel sign language videos and spoken language subtitles. |
Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset (2024.lrec-main)
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| Challenge: | Using Swiss Judgement Prediction, we evaluate the explainability of state-of-the-art monolingual and multilingual LJP models. |
| Approach: | They propose an occlusion-based approach to evaluate the explainability performance of legal judgement prediction models using Swiss Judgement Prediction, the only available multilingual LJP dataset. |
| Outcome: | The proposed framework allows us to quantify the influence of lower court information on model predictions, exposing current models’ biases. |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |