Papers by Stephan Oepen
A New Massive Multilingual Dataset for High-Performance Language Technologies (2024.lrec-main)
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Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, Jörg Tiedemann
| Challenge: | a new massive multilingual dataset is available for language modeling and machine translation training. |
| Approach: | They present a massive multilingual dataset using web crawls from the Internet Archive and CommonCrawl . they use open-source software tools and high-performance computing to acquire, manage and process large corpora . |
| Outcome: | The HPLT language resources is a massive multilingual dataset . it includes monolingual and bilingual corpora extracted from CommonCrawl and the Internet Archive . the results are published online at the journal journal cense4 . |
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT) (2025.acl-long)
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Laurie Burchell, Ona De Gibert Bonet, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya Malik, Farrokh Mehryary, Vladislav Mikhailov, Nikita Moghe, Amanda Myntti, Dayyán O’Brien, Stephan Oepen, Proyag Pal, Jousia Piha, Sampo Pyysalo, Gema Ramírez-Sánchez, David Samuel, Pavel Stepachev, Jörg Tiedemann, Dušan Variš, Tereza Vojtěchová, Jaume Zaragoza-Bernabeu
| Challenge: | a large number of textual data is needed to train state-of-the-art large language models. |
| Approach: | They propose a collection of monolingual and parallel corpora from the Internet Archive . they document the entire data pipeline and release the code to reproduce it . |
| Outcome: | The proposed collection of monolingual and parallel corpora is based on the HPLT v2 dataset . it includes 8T tokens covering 193 languages and 380M sentence pairs covering 51 languages . |
Direct parsing to sentiment graphs (2022.acl-short)
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| Challenge: | Existing methods for structured sentiment analysis (SSA) focus on subcomponents of sentiment graphs without explicitly expressing their relations or the polarity. |
| Approach: | They propose a graph-based semantic parser which directly predicts sentiment graphs from text without reliance on lossy conversions to intermediate dependency representations. |
| Outcome: | The proposed model performs on 4 out of 5 standard benchmark sets and compares with dependency-based models on the more structurally complex datasets. |
Graph-Based Meaning Representations: Design and Processing (P19-4)
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| Challenge: | This tutorial focuses on representing and processing sentence meaning in the form of labeled directed graphs. |
| Approach: | This tutorial will briefly review relevant background in formal and linguistic semantics . it will also briefly define a unified abstract view on different flavors of semantic graphs - and associated terminology . |
| Outcome: | The tutorial will briefly review relevant background in formal and linguistic semantics . |
A Tale of Three Parsers: Towards Diagnostic Evaluation for Meaning Representation Parsing (2020.lrec-1)
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| Challenge: | Empirical results suggest that the proposed methodology can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different approaches. |
| Approach: | They propose to map from natural language utterances to graph-based encodings of its semantic structure using contrastive and diagnostic evaluation techniques. |
| Outcome: | The proposed method can be meaningfully applied to parsing into graph-structured target representations, uncovering hitherto unknown properties of the different systems that can inform future development and cross-fertilization across approaches. |
Structured Sentiment Analysis as Dependency Graph Parsing (2021.acl-long)
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| Challenge: | Structured sentiment analysis attempts to extract full opinion tuples from a text, but has been subdivided into smaller and smaller sub-tasks, e.g., target extraction or targeted polarity classification. |
| Approach: | They propose a framework which jointly predicts all elements of an opinion tuple and their relations by using dependency graph parsing. |
| Outcome: | The proposed framework improves on five datasets in English, Norwegian, Basque, and Catalan and refining the sentiment graphs with syntactic dependency information further improves results. |
Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (D18-1)
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| Challenge: | In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem. |
| Approach: | They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task. |
| Outcome: | The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations. |