---
title: "University of Amsterdam — works"
description: "71 works by researchers of University of Amsterdam in our library, the most cited first."
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# Works of University of Amsterdam

71 works · the most cited first · [← University of Amsterdam](https://md.research.elamur.ai/free/en/universities/university-of-amsterdam/)

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. [**Auto-Encoding Variational Bayes**Diederik P Kingma, Max Welling*2013 · 14,991 citations*](https://md.research.elamur.ai/free/en/papers/kingma-2013-auto-encoding-variational-bayes/)
3. [**Semi-Supervised Classification with Graph Convolutional Networks**Thomas N. Kipf, Max Welling*2016 · 8,809 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
4. [**Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned**Elena Voita, David Talbot, Fedor Moiseev, et al.*2019 · 1,511 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-analyzing-multi-head-self-attention/)
5. [**Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling**Diego Marcheggiani, Ivan Titov*2017 · 1,101 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/)
6. [**Quantifying Attention Flow in Transformers**Samira Abnar, Willem Zuidema*2020 · 1,049 citations*](https://md.research.elamur.ai/free/en/papers/abnar-2020-quantifying-attention-flow-transformers/)
7. [**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/)
8. [**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/)
9. [**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/)
10. [**The stages of event extraction**David Ahn*2006 · 704 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2006-stages-event-extraction/)
11. [**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/)
12. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
13. [**The LAMBADA dataset: Word prediction requiring a broad discourse context**Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, et al.*2016 · 506 citations*](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/)
14. [**Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation**Biao Zhang, Philip Williams, Ivan Titov, et al.*2020 · 472 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-improving-massively-multilingual-neural-machine/)
15. [**Findings of the 2015 Workshop on Statistical Machine Translation**Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al.*2015 · 461 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2015-findings-2015-workshop-statistical-machine/)
16. [**Context-Aware Neural Machine Translation Learns Anaphora Resolution**Elena Voita, Pavel Serdyukov, Rico Sennrich, et al.*2018 · 446 citations*](https://md.research.elamur.ai/free/en/papers/voita-2018-context-aware-neural-machine-translation/)
17. [**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/)
18. [**Neural versus Phrase-Based Machine Translation Quality: a Case Study**Luisa Bentivogli, Arianna Bisazza, Mauro Cettolo, et al.*2016 · 436 citations*](https://md.research.elamur.ai/free/en/papers/bentivogli-2016-neural-versus-phrase-based-machine/)
19. [**Data Augmentation for Low-Resource Neural Machine Translation**Marzieh Fadaee, Arianna Bisazza, Christof Monz*2017 · 419 citations*](https://md.research.elamur.ai/free/en/papers/fadaee-2017-data-augmentation-low-resource-neural/)
20. [**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/)
21. [**Long Range Arena: A Benchmark for Efficient Transformers**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2020 · 396 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-long-range-arena-benchmark-efficient/)
22. [**BPE-Dropout: Simple and Effective Subword Regularization**Ivan Provilkov, Dmitrii Emelianenko, Elena Voita*2020 · 389 citations*](https://md.research.elamur.ai/free/en/papers/provilkov-2020-bpe-dropout-simple-effective-subword/)
23. [**A Shared Task on Multimodal Machine Translation and Crosslingual Image Description**Lucia Specia, Stella Frank, Khalil Sima'an, et al.*2016 · 351 citations*](https://md.research.elamur.ai/free/en/papers/specia-2016-shared-task-multimodal-machine-translation/)
24. [**Information-Theoretic Probing with Minimum Description Length**Elena Voita, Ivan Titov*2020 · 339 citations*](https://md.research.elamur.ai/free/en/papers/voita-2020-information-theoretic-probing-minimum-description/)
25. [**When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion**Elena Voita, Rico Sennrich, Ivan Titov*2019 · 321 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-when-good-translation-is-wrong/)
26. [**Analysing Lexical Semantic Change with Contextualised Word Representations**Mario Giulianelli, Marco Del Tredici, Raquel Fernández*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2020-analysing-lexical-semantic-change-contextualised/)
27. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
28. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning**Petter Tornberg*2023 · 230 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
29. [**Open-Domain Question Answering Goes Conversational via Question Rewriting**Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, et al.*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/)
30. [**Findings of the Third Shared Task on Multimodal Machine Translation**Loïc Barrault, Fethi Bougares, Lucia Specia, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/barrault-2018-findings-third-shared-task-multimodal/)
31. [**The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives**Elena Voita, Rico Sennrich, Ivan Titov*2019 · 222 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-bottom-up-evolution-representations-transformer/)
32. [**Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing**Edoardo Maria Ponti, Helen O'Horan, Yevgeni Berzak, et al.*2019 · 217 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2019-modeling-language-variation-universals-survey/)
33. [**Results of the WMT17 Metrics Shared Task**Ondřej Bojar, Yvette Graham, Amir Kamran*2017 · 210 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2017-results-wmt17-metrics-shared-task/)
34. [**The emergence of number and syntax units in LSTM language models**Yair Lakretz, German Kruszewski, Theo Desbordes, et al.*2019 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lakretz-2019-emergence-number-syntax-units-lstm/)
35. [**Graph Convolutional Encoders for Syntax-aware Neural Machine Translation**Jasmijn Bastings, Ivan Titov, Wilker Aziz, et al.*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/bastings-2017-graph-convolutional-encoders-syntax-aware/)
36. [**Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet**Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, et al.*2024 · 177 citations*](https://md.research.elamur.ai/free/en/papers/kocmi-2024-findings-wmt24-general-machine-translation/)
37. [**Results of the WMT16 Metrics Shared Task**Ondřej Bojar, Yvette Graham, Amir Kamran, et al.*2016 · 175 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2016-results-wmt16-metrics-shared-task/)
38. [**A Report on the 2018 VUA Metaphor Detection Shared Task**Chee Wee (Ben) Leong, Beata Beigman Klebanov, Ekaterina Shutova*2018 · 167 citations*](https://md.research.elamur.ai/free/en/papers/leong-2018-report-2018-vua-metaphor-detection/)
39. [**Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation**Bryan Eikema, Wilker Aziz*2020 · 165 citations*](https://md.research.elamur.ai/free/en/papers/eikema-2020-is-map-decoding-all-you/)
40. [**Interpretable Neural Predictions with Differentiable Binary Variables**Jasmijn Bastings, Wilker Aziz, Ivan Titov*2019 · 126 citations*](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/)
41. [**A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun Translation in Neural Machine Translation**Mathias Müller, Annette Rios, Elena Voita, et al.*2018 · 123 citations*](https://md.research.elamur.ai/free/en/papers/muller-2018-large-scale-test-set-evaluation/)
42. [**Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation**Verna Dankers, Christopher Lucas, Ivan Titov*2022 · 105 citations*](https://md.research.elamur.ai/free/en/papers/dankers-2022-can-transformer-be-too-compositional/)
43. [**A Rule Based Approach to Discourse Parsing**Livia Polanyi, Chris Culy, Martin van den Berg, et al.*2004 · 94 citations*](https://md.research.elamur.ai/free/en/papers/polanyi-2004-rule-based-approach-discourse-parsing/)
44. [**A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling**Diego Marcheggiani, Anton Frolov, Ivan Titov*2017 · 86 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-simple-accurate-syntax-agnostic-neural/)
45. [**Findings of the WMT25 General Machine Translation Shared Task: Time to Stop Evaluating on Easy Test Sets**Tom Kocmi, Ekaterina Artemova, Eleftherios Avramidis, et al.*2025 · 73 citations*](https://md.research.elamur.ai/free/en/papers/kocmi-2025-findings-wmt25-general-machine-translation/)
46. [**Integrated Morphological and Syntactic Disambiguation for Modern Hebrew**Reut Tsarfaty*2006 · 67 citations*](https://md.research.elamur.ai/free/en/papers/tsarfaty-2006-integrated-morphological-syntactic-disambiguation-modern/)
47. [**The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse**Pere-Lluís Huguet Cabot, Verna Dankers, David Abadi, et al.*2020 · 64 citations*](https://md.research.elamur.ai/free/en/papers/cabot-2020-pragmatics-behind-politics-modelling-metaphor/)
48. [**Learning a POS tagger for AAVE-like language**Anna Jørgensen, Dirk Hovy, Anders Søgaard*2016 · 59 citations*](https://md.research.elamur.ai/free/en/papers/jorgensen-2016-learning-pos-tagger-aave-like/)
49. [**The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue**Janosch Haber, Tim Baumgärtner, Ece Takmaz, et al.*2019 · 51 citations*](https://md.research.elamur.ai/free/en/papers/haber-2019-photobook-dataset-building-common-ground/)
50. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
51. [**Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew**Roy Bar-Haim, Khalil Sima'an, Yoad Winter*2005 · 46 citations*](https://md.research.elamur.ai/free/en/papers/haim-2005-choosing-optimal-architecture-segmentation-pos/)
52. [**Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis**Mario Giulianelli, Iris Luden, Raquel Fernandez, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2023-definition-generation-senses/)
53. [**Discourse particles in early English: Clause structure, pragmatics and discourse management**Ans van Kemenade, Meta Links*2020 · 37 citations*](https://md.research.elamur.ai/free/en/papers/kemenade-2020-discourse-particles-early-english-clause/)
54. [**Smoothing a Lexicon-based POS Tagger for Arabic and Hebrew**Saib Manour, Khalil Sima'an, Yoad Winter*2007 · 34 citations*](https://md.research.elamur.ai/free/en/papers/manour-2007-smoothing-lexicon-based-pos-tagger/)
55. [**Becoming Linguistically Mature: Modeling English and German Children's Writing Development Across School Grades**Elma Kerz, Yu Qiao, Daniel Wiechmann, et al.*2020 · 27 citations*](https://md.research.elamur.ai/free/en/papers/kerz-2020-becoming-linguistically-mature-modeling-english/)
56. [**Word-Based or Morpheme-Based? Annotation Strategies for Modern Hebrew Clitics**Reut Tsarfaty, Yoav Goldberg*2008 · 23 citations*](https://md.research.elamur.ai/free/en/papers/tsarfaty-2008-word-based-or-morpheme-based/)
57. [**Modeling Morphosyntactic Agreement in Constituency-Based Parsing of Modern Hebrew**Reut Tsarfaty, Khalil Sima'an*2010 · 21 citations*](https://md.research.elamur.ai/free/en/papers/tsarfaty-2010-modeling-morphosyntactic-agreement-constituency-based/)
58. [**The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions**Joachim Daiber, Rob van der Goot*2016 · 19 citations*](https://md.research.elamur.ai/free/en/papers/daiber-2016-denoised-web-treebank-evaluating-dependency/)
59. [**Towards Pragmatic Production Strategies for Natural Language Generation Tasks**Mario Giulianelli*2022 · 12 citations*](https://md.research.elamur.ai/free/en/papers/giulianelli-2022-pragmatic-production-strategies-natural-language/)
60. [**WN-Salience: A Corpus of News Articles with Entity Salience Annotations**Chuan Wu, Evangelos Kanoulas, Maarten de Rijke, et al.*2020 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-wn-salience-corpus-news-articles/)
61. [**A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples**Yuxuan Wang, Wanxiang Che, Ivan Titov, et al.*2021 · 8 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-closer-look-into-robustness-neural/)
62. [**Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy**Andrei C. Aioanei, Regine Hunziker-Rodewald, Konstantin Klein, et al.*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/aioanei-2023-deep-aramaic-towards-synthetic-data/)
63. [**LAF-Fabric: a data analysis tool for Linguistic Annotation Framework with an application to the Hebrew Bible**Dirk Roorda, Gino Kalkman, Martijn Naaijer, et al.*2014 · 7 citations*](https://md.research.elamur.ai/free/en/papers/roorda-2014-laf-fabric-data-analysis-tool/)
64. [**Many systems, one strategy: Acquiring ordinals in Dutch and English**Caitlin Meyer, Sjef Barbiers, Fred Weerman*2020 · 6 citations*](https://md.research.elamur.ai/free/en/papers/meyer-2020-many-systems-one-strategy-acquiring/)
65. [**Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations**Shaomu Tan, Ryosuke Mitani, Ritvik Choudhary, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/tan-2026-remedy-r-generative-reasoning-machine/)
66. [**Plan-Guided Text Simplification with Extended Contexts**Pascal Mathas, Jan Bakker, Jaap Kamps*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mathas-2026-plan-guided-text-simplification-extended/)
67. [**A framework for annotating and modelling intentions behind metaphor use**Gianluca Michelli, Xiaoyu Tong, Ekaterina Shutova*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/michelli-2026-framework-annotating-modelling-intentions-behind/)
68. [**SimLex-999 for Modern Greek**Leonidas Mylonadis, Jelke Bloem*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mylonadis-2026-simlex-999-modern-greek/)
69. [**Human-like Fleeting Memory Improves Language Learning but Impairs Reading Time Prediction in Transformer Language Models**Abishek Thamma, Micha Heilbron*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/thamma-2026-human-like-fleeting-memory-improves/)
70. [**Assessing Small Language Models as Text Simplification Evaluators**David Carranza Navarrete, Jan Bakker, Jaap Kamps*2026*](https://md.research.elamur.ai/free/en/papers/navarrete-2026-assessing-small-language-models-as/)
71. [**Lost in Translation: Repurposing semantic similarity benchmarks for evaluating lexical-semantic consistency in LLM-based machine translation**Quin Ye, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/ye-2026-lost-translation-repurposing-semantic-similarity/)
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