---
title: "Germany — works"
description: "Works of Germany: 550 in our library · page 1 of 6, the most cited first."
canonical: "https://research.elamur.ai/free/en/countries/germany/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/countries/germany/works/)

[home](https://md.research.elamur.ai/free/en/) / [countries](https://md.research.elamur.ai/free/en/countries/) / [Germany](https://md.research.elamur.ai/free/en/countries/germany/) / works

# Works of Germany

550 · page 1 of 6 · the most cited first · [← Germany](https://md.research.elamur.ai/free/en/countries/germany/)

**1** [2](https://md.research.elamur.ai/free/en/countries/germany/works/page/2/) … [6](https://md.research.elamur.ai/free/en/countries/germany/works/page/6/) [next →](https://md.research.elamur.ai/free/en/countries/germany/works/page/2/)

1. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
2. [**Neural Machine Translation by Jointly Learning to Align and Translate**Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio*2014 · 18,908 citations*](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
3. [**Text Categorization with Support Vector Machines: Learning with Many Relevant Features**Thorsten Joachims*1998 · 8,304 citations*](https://md.research.elamur.ai/free/en/papers/joachims-1998-text-categorization-support-vector-machines/)
4. [**On the Properties of Neural Machine Translation: Encoder-Decoder Approaches**Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, et al.*2014 · 7,734 citations*](https://md.research.elamur.ai/free/en/papers/cho-2014-properties-neural-machine-translation-encoder/)
5. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,429 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
6. [**Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/)
7. [**A Call for Clarity in Reporting BLEU Scores**Matt Post*2018 · 4,130 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
8. [**Lexicon-Based Methods for Sentiment Analysis**Maite Taboada, Julian Brooke, Milan Tofiloski, et al.*2011 · 3,556 citations*](https://md.research.elamur.ai/free/en/papers/taboada-2011-lexicon-based-methods-sentiment-analysis/)
9. [**Continual Lifelong Learning with Neural Networks: A Review**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,210 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
10. [**Exploring the Space of Topic Coherence Measures**Michael Röder, Andreas Both, Alexander Hinneburg*2015 · 2,368 citations*](https://md.research.elamur.ai/free/en/papers/roder-2015-exploring-space-topic-coherence-measures/)
11. [**chrF: character n-gram F-score for automatic MT evaluation**Maja Popović*2015 · 2,105 citations*](https://md.research.elamur.ai/free/en/papers/popovic-2015-chrf-character-n-gram-f/)
12. [**Knowledge Graphs**Aidan Hogan, Eva Blomqvist, Michael Cochez, et al.*2020 · 2,011 citations*](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
13. [**A Survey on Hate Speech Detection using Natural Language Processing**Anna Schmidt, Michael Wiegand*2017 · 1,799 citations*](https://md.research.elamur.ai/free/en/papers/schmidt-2017-survey-hate-speech-detection-natural/)
14. [**Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference**Timo Schick, Hinrich Schütze*2021 · 1,796 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/)
15. [**Attention-Based Models for Speech Recognition**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,776 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
16. [**TnT – A Statistical Part-of-Speech Tagger**Thorsten Brants*2000 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/brants-2000-tnt-statistical-part-speech-tagger/)
17. [**Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding**Akira Fukui, Dong Huk Park, Daylen Yang, et al.*2016 · 1,642 citations*](https://md.research.elamur.ai/free/en/papers/fukui-2016-multimodal-compact-bilinear-pooling-visual/)
18. [**How Can We Know What Language Models Know?**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,493 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/)
19. [**Improved Statistical Alignment Models**Franz Josef Och, Hermann Ney*2000 · 1,467 citations*](https://md.research.elamur.ai/free/en/papers/och-2000-improved-statistical-alignment-models/)
20. [**ELAN: a Professional Framework for Multimodality Research**Peter Wittenburg, Hennie Brugman, Albert Russel, et al.*2006 · 1,427 citations*](https://md.research.elamur.ai/free/en/papers/wittenburg-2006-elan-professional-framework-multimodality-research/)
21. [**Fast Graph Representation Learning with PyTorch Geometric**Matthias Fey, Jan Eric Lenssen*2019 · 1,413 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
22. [**Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data**Emily M. Bender, Alexander Koller*2020 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/)
23. [**Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation**Nils Reimers, Iryna Gurevych*2020 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2020-making-monolingual-sentence-embeddings-multilingual/)
24. [**It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners**Timo Schick, Hinrich Schütze*2021 · 1,118 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/)
25. [**ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,048 citations*](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/)
26. [**AdapterFusion: Non-Destructive Task Composition for Transfer Learning**Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.*2020 · 948 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterfusion-non-destructive-task-composition/)
27. [**Findings of the 2016 Conference on Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2016 · 936 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/)
28. [**Visual7W: Grounded Question Answering in Images**Yuke Zhu, Oliver Groth, Michael Bernstein, et al.*2015 · 915 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-visual7w-grounded-question-answering-images/)
29. [**Findings of the 2019 Conference on Machine Translation (WMT19)**Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, et al.*2019 · 839 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2019-findings-2019-conference-machine-translation/)
30. [**MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer**Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, et al.*2020 · 831 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-mad-x-adapter-based-framework/)
31. [**MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance**Wei Zhao, Maxime Peyrard, Fei Liu, et al.*2019 · 822 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-moverscore-text-generation-evaluating-contextualized/)
32. [**U-Net: Convolutional Networks for Biomedical Image Segmentation**Olaf Ronneberger, Philipp Fischer, Thomas Brox*2015 · 813 citations*](https://md.research.elamur.ai/free/en/papers/ronneberger-2015-u-net-convolutional-networks-biomedical/)
33. [**A Stylometric Inquiry into Hyperpartisan and Fake News**Martin Potthast, Johannes Kiesel, Kevin Reinartz, et al.*2018 · 808 citations*](https://md.research.elamur.ai/free/en/papers/potthast-2018-stylometric-inquiry-into-hyperpartisan-fake/)
34. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
35. [**chrF++: words helping character n-grams**Maja Popović*2017 · 749 citations*](https://md.research.elamur.ai/free/en/papers/popovic-2017-chrf-words-helping-character-n/)
36. [**Findings of the 2018 Conference on Machine Translation (WMT18)**Ondřej Bojar, Christian Federmann, Mark Fishel, et al.*2018 · 747 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2018-findings-2018-conference-machine-translation/)
37. [**AdapterHub: A Framework for Adapting Transformers**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 747 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/)
38. [**Findings of the 2017 Conference on Machine Translation (WMT17)**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2017 · 685 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2017-findings-2017-conference-machine-translation/)
39. [**Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences**Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/)
40. [**Automatic Identification of Word Translations from Unrelated English and German Corpora**Reinhard Rapp*1999 · 659 citations*](https://md.research.elamur.ai/free/en/papers/rapp-1999-automatic-identification-word-translations-unrelated/)
41. [**Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging**Nils Reimers, Iryna Gurevych*2017 · 648 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2017-reporting-score-distributions-makes-difference/)
42. [**The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages**Jan Hajič, Massimiliano Ciaramita, Richard Johansson, et al.*2009 · 631 citations*](https://md.research.elamur.ai/free/en/papers/hajic-2009-conll-2009-shared-task-syntactic/)
43. [**Object Hallucination in Image Captioning**Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, et al.*2018 · 617 citations*](https://md.research.elamur.ai/free/en/papers/rohrbach-2018-object-hallucination-image-captioning/)
44. [**Parsing Argumentation Structures in Persuasive Essays**Christian Stab, Iryna Gurevych*2017 · 613 citations*](https://md.research.elamur.ai/free/en/papers/stab-2017-parsing-argumentation-structures-persuasive-essays/)
45. [**CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies**Daniel Zeman, Martin Popel, Milan Straka, et al.*2017 · 604 citations*](https://md.research.elamur.ai/free/en/papers/zeman-2017-conll-2017-shared-task-multilingual/)
46. [**A simple neural network module for relational reasoning**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 590 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
47. [**Top Accuracy and Fast Dependency Parsing is not a Contradiction**Bernd Bohnet*2010 · 583 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2010-top-accuracy-fast-dependency-parsing/)
48. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 556 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
49. [**Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields**Niklas Jakob, Iryna Gurevych*2010 · 545 citations*](https://md.research.elamur.ai/free/en/papers/jakob-2010-extracting-opinion-targets-single-cross/)
50. [**Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages**Dirk Goldhahn, Thomas Eckart, Uwe Quasthoff*2012 · 533 citations*](https://md.research.elamur.ai/free/en/papers/goldhahn-2012-building-large-monolingual-dictionaries-leipzig/)
51. [**LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs**Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al.*2021 · 528 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2021-laion-400m-open-dataset-clip/)
52. [**Unsupervised Multilingual Sentence Boundary Detection**Tibor Kiss, Jan Strunk*2006 · 523 citations*](https://md.research.elamur.ai/free/en/papers/kiss-2006-unsupervised-multilingual-sentence-boundary-detection/)
53. [**Using Semantic Roles to Improve Question Answering**Dan Shen, Mirella Lapata*2007 · 522 citations*](https://md.research.elamur.ai/free/en/papers/shen-2007-semantic-roles-improve-question-answering/)
54. [**Breaking NLI Systems with Sentences that Require Simple Lexical Inferences**Max Glockner, Vered Shwartz, Yoav Goldberg*2018 · 517 citations*](https://md.research.elamur.ai/free/en/papers/glockner-2018-breaking-nli-systems-sentences-that/)
55. [**Identifying Argumentative Discourse Structures in Persuasive Essays**Christian Stab, Iryna Gurevych*2014 · 517 citations*](https://md.research.elamur.ai/free/en/papers/stab-2014-identifying-argumentative-discourse-structures-persuasive/)
56. [**Inducing Crosslingual Distributed Representations of Words**Alexandre Klementiev, Ivan Titov, Binod Bhattarai*2012 · 501 citations*](https://md.research.elamur.ai/free/en/papers/klementiev-2012-inducing-crosslingual-distributed-representations-words/)
57. [**HateBERT: Retraining BERT for Abusive Language Detection in English**Tommaso Caselli, Valerio Basile, Jelena Mitrović, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/caselli-2021-hatebert-retraining-bert-abusive-language/)
58. [**XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning**Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.*2020 · 465 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/)
59. [**Large language models are few-shot clinical information extractors**Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.*2022 · 448 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/)
60. [**Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets**Julia Kreutzer, Isaac Caswell, Lisa Wang, et al.*2021 · 447 citations*](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/)
61. [**Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference**Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, et al.*2019 · 444 citations*](https://md.research.elamur.ai/free/en/papers/falke-2019-ranking-generated-summaries-correctness-interesting/)
62. [**How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models**Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/)
63. [**Findings of the 2013 Workshop on Statistical Machine Translation**Ondřej Bojar, Christian Buck, Chris Callison-Burch, et al.*2013 · 441 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2013-findings-2013-workshop-statistical-machine/)
64. [**Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation**Matt Post, David Vilar*2018 · 437 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-fast-lexically-constrained-decoding-dynamic/)
65. [**PATTY: A Taxonomy of Relational Patterns with Semantic Types**Ndapandula Nakashole, Gerhard Weikum, Fabian Suchanek*2012 · 432 citations*](https://md.research.elamur.ai/free/en/papers/nakashole-2012-patty-taxonomy-relational-patterns-semantic/)
66. [**Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly**Nora Kassner, Hinrich Schütze*2020 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/)
67. [**MasakhaNER: Named Entity Recognition for African Languages**David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, et al.*2021 · 429 citations*](https://md.research.elamur.ai/free/en/papers/adelani-2021-masakhaner-named-entity-recognition-african/)
68. [**Text-Translation Alignment**Martin Kay, Martin Roscheisen*1993 · 423 citations*](https://md.research.elamur.ai/free/en/papers/kay-1993-text-translation-alignment/)
69. [**The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation**Jan-Christoph Klie, Michael Bugert, Beto Boullosa, et al.*2018 · 422 citations*](https://md.research.elamur.ai/free/en/papers/klie-2018-inception-platform-machine-assisted-knowledge/)
70. [**75 Languages, 1 Model: Parsing Universal Dependencies Universally**Dan Kondratyuk, Milan Straka*2019 · 415 citations*](https://md.research.elamur.ai/free/en/papers/kondratyuk-2019-75-languages-1-model-parsing/)
71. [**An Annotation Scheme for Free Word Order Languages**Wojciech Skut, Brigitte Krenn, Thorsten Brants, et al.*1997 · 408 citations*](https://md.research.elamur.ai/free/en/papers/skut-1997-annotation-scheme-free-word-order/)
72. [**Annotating Argument Components and Relations in Persuasive Essays**Christian Stab, Iryna Gurevych*2014 · 403 citations*](https://md.research.elamur.ai/free/en/papers/stab-2014-annotating-argument-components-relations-persuasive/)
73. [**Findings of the 2020 Conference on Machine Translation (WMT20)**Loïc Barrault, Magdalena Biesialska, Ondřej Bojar, et al.*2020 · 401 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2020-findings-2020-conference-machine-translation/)
74. [**Depression and Self-Harm Risk Assessment in Online Forums**Andrew Yates, Arman Cohan, Nazli Goharian*2017 · 401 citations*](https://md.research.elamur.ai/free/en/papers/yates-2017-depression-self-harm-risk-assessment/)
75. [**A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
76. [**Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder**Thanh-Le Ha, Jan Niehues, Alex Waibel*2016 · 398 citations*](https://md.research.elamur.ai/free/en/papers/ha-2016-multilingual-neural-machine-translation-universal/)
77. [**The Parallel Grammar Project**Miriam Butt, Helge Dyvik, Tracy Holloway King, et al.*2002 · 392 citations*](https://md.research.elamur.ai/free/en/papers/butt-2002-parallel-grammar-project/)
78. [**DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning**Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, et al.*2018 · 388 citations*](https://md.research.elamur.ai/free/en/papers/popat-2018-declare-debunking-fake-news-false/)
79. [**SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles**Giovanni Da San Martino, Alberto Barrón-Cedeño, Henning Wachsmuth, et al.*2020 · 371 citations*](https://md.research.elamur.ai/free/en/papers/martino-2020-semeval-2020-task-11-detection/)
80. [**The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text**Richárd Farkas, Veronika Vincze, György Móra, et al.*2010 · 370 citations*](https://md.research.elamur.ai/free/en/papers/farkas-2010-conll-2010-shared-task-learning/)
81. [**An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research**Sonja Nießen, Franz Josef Och, Gregor Leusch, et al.*2000 · 366 citations*](https://md.research.elamur.ai/free/en/papers/niessen-2000-evaluation-tool-machine-translation-fast/)
82. [**A Transition-Based System for Joint Part-of-Speech Tagging and Labeled Non-Projective Dependency Parsing**Bernd Bohnet, Joakim Nivre*2012 · 364 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2012-transition-based-system-joint-part/)
83. [**The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation**Barbara Plank*2022 · 364 citations*](https://md.research.elamur.ai/free/en/papers/plank-2022-problem-human-label-variation-ground/)
84. [**Datasets: A Community Library for Natural Language Processing**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 358 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
85. [**Exploring Neural Text Simplification Models**Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, et al.*2017 · 353 citations*](https://md.research.elamur.ai/free/en/papers/nisioi-2017-exploring-neural-text-simplification-models/)
86. [**SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection**Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, et al.*2020 · 351 citations*](https://md.research.elamur.ai/free/en/papers/schlechtweg-2020-semeval-2020-task-1-unsupervised/)
87. [**RobBERT: a Dutch RoBERTa-based Language Model**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 346 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
88. [**Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution**Simone Paolo Ponzetto, Michael Strube*2006 · 346 citations*](https://md.research.elamur.ai/free/en/papers/ponzetto-2006-exploiting-semantic-role-labeling-wordnet/)
89. [**SemEval-2019 Task 4: Hyperpartisan News Detection**Johannes Kiesel, Maria Mestre, Rishabh Shukla, et al.*2019 · 335 citations*](https://md.research.elamur.ai/free/en/papers/kiesel-2019-semeval-2019-task-4-hyperpartisan/)
90. [**Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages**Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, et al.*2020 · 333 citations*](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)
91. [**Do Supervised Distributional Methods Really Learn Lexical Inference Relations?**Omer Levy, Steffen Remus, Chris Biemann, et al.*2015 · 325 citations*](https://md.research.elamur.ai/free/en/papers/levy-2015-do-supervised-distributional-methods-really/)
92. [**MultiUN: A Multilingual Corpus from United Nation Documents**Andreas Eisele, Yu Chen*2010 · 323 citations*](https://md.research.elamur.ai/free/en/papers/eisele-2010-multiun-multilingual-corpus-united-nation/)
93. [**"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models**Xinyue Shen, Zeyuan Chen, Michael Backes, et al.*2023 · 320 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/)
94. [**Probing Pretrained Language Models for Lexical Semantics**Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, et al.*2020 · 320 citations*](https://md.research.elamur.ai/free/en/papers/vulic-2020-probing-pretrained-language-models-lexical/)
95. [**CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies**Daniel Zeman, Jan Hajič, Martin Popel, et al.*2018 · 316 citations*](https://md.research.elamur.ai/free/en/papers/zeman-2018-conll-2018-shared-task-multilingual/)
96. [**Statistical Approaches to Computer-Assisted Translation**Sergio Barrachina, Oliver Bender, Francisco Casacuberta, et al.*2008 · 313 citations*](https://md.research.elamur.ai/free/en/papers/barrachina-2009-statistical-approaches-computer-assisted-translation/)
97. [**On Improving the Accuracy of Readability Classification using Insights from Second Language Acquisition**Sowmya Vajjala, Detmar Meurers*2012 · 311 citations*](https://md.research.elamur.ai/free/en/papers/vajjala-2012-improving-accuracy-readability-classification-insights/)
98. [**Computational Argumentation Quality Assessment in Natural Language**Henning Wachsmuth, Nona Naderi, Yufang Hou, et al.*2017 · 306 citations*](https://md.research.elamur.ai/free/en/papers/wachsmuth-2017-computational-argumentation-quality-assessment-natural/)
99. [**Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM**Ivan Habernal, Iryna Gurevych*2016 · 302 citations*](https://md.research.elamur.ai/free/en/papers/habernal-2016-which-argument-is-more-convincing/)
100. [**A Comparison of Alignment Models for Statistical Machine Translation**Franz Josef Och, Hermann Ney*2000 · 300 citations*](https://md.research.elamur.ai/free/en/papers/och-2000-comparison-alignment-models-statistical-machine/)

**1** [2](https://md.research.elamur.ai/free/en/countries/germany/works/page/2/) … [6](https://md.research.elamur.ai/free/en/countries/germany/works/page/6/) [next →](https://md.research.elamur.ai/free/en/countries/germany/works/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/countries/germany/) — Germany