Papers by Rocco Tripodi
Evaluating Multilingual Sentence Representation Models in a Real Case Scenario (2022.lrec-1)
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| Challenge: | a recent study has shown that the infamous Protocols are actually plagiarized . a convoluted task with no standard benchmarks for paraphrase detection and sentence similarity is a problem . |
| Approach: | They evaluate sentence representation models on the paraphrase detection task . they use a forged text from the so-called "Protocols of the Elders of Zion" scholars have demonstrated that the first text plagiarizes from the second . |
| Outcome: | The proposed model is based on the forged “Protocols of the Elders of Zion” . the model is similar to the standard model but has some problems . |
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation (2021.naacl-main)
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| Challenge: | Graph-based semantic parsing is one of the most promising general-purpose meaning representations . owing to this heterogeneity, most research focused on solutions specific to a given formalism . |
| Approach: | They propose a multilingual neural machine translation framework for Graph-based semantic parsing . they propose Graph2seq architecture that trains with an MNMT objective . |
| Outcome: | The proposed framework outperforms all competitors on cross-lingual parsing tasks. |
GeneSis: A Generative Approach to Substitutes in Context (2021.emnlp-main)
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| Challenge: | lexical substitution tasks require a system to provide adequate replacements for a word in a given context. |
| Approach: | They propose a generative approach to lexical substitution using a seq2seq model to generate suitable replacements for a word in context. |
| Outcome: | The proposed approach achieves state-of-the-art on different benchmarks and human evaluation of the generated substitutes. |
XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
Latent vs Explicit Knowledge Representation: How ChatGPT Answers Questions about Low-Frequency Entities (2024.lrec-main)
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| Challenge: | In this paper, we compare two different approaches to the free-form Question Answering task. |
| Approach: | They propose to use a new benchmark to test knowledge representations on a dynamic benchmark. |
| Outcome: | The proposed benchmark is particularly challenging and the best model answers only on 50% of the questions. |
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling (2021.findings-emnlp)
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| Challenge: | Multilingual and cross-lingual Semantic Role Labeling (SRL) has attracted increasing attention as multilingual text representation techniques have become more effective and widely available. |
| Approach: | They propose a benchmark for multilingual and cross-lingual, span- and dependency-based SRL that provides expert-curated parallel annotations using a common predicate-argument structure inventory. |
| Outcome: | The proposed benchmark provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL. |
Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation (D19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing (NLP). |
| Approach: | They propose a game-theoretic model that embeds ambiguous words as players of a non cooperative game and their senses as strategies that the players can select in order to play the games. |
| Outcome: | The proposed model performs well on standard benchmarks and different tests on standard datasets. |
KE-MHISTO: Towards a Multilingual Historical Knowledge Extraction Benchmark for Addressing the Long-Tail Problem (2025.findings-acl)
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Arianna Graciotti, Leonardo Piano, Nicolas Lazzari, Enrico Daga, Rocco Tripodi, Valentina Presutti, Livio Pompianu
| Challenge: | Large Language Models struggle when probed for long-tail knowledge due to the inherent sparsity of such data. |
| Approach: | They propose a multilingual benchmark for Entity Linking and Question Answering in the domain of historical music knowledge that provides broader coverage of long-tail knowledge. |
| Outcome: | The proposed model provides broader coverage of long-tail knowledge compared to existing models. |