---
title: "Who cites Statistical Significance Tests for Machine…"
description: "1,049 papers of the Elamur Research library cite «Statistical Significance Tests for Machine Translation Evaluation», found in their bibliographies; the…"
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# Who cites «Statistical Significance Tests for Machine Translation Evaluation»

1,049 papers of our library cite it (of the 140,884 whose bibliographies we read) · 78 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/koehn-2004-statistical-significance-tests-machine-translation/)

1. [**A Hierarchical Phrase-Based Model for Statistical Machine Translation**David Chiang*2005 · 1,683 citations*](https://md.research.elamur.ai/free/en/papers/chiang-2005-hierarchical-phrase-based-model-statistical/)
2. [**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/)
3. [**Clause Restructuring for Statistical Machine Translation**Michael Collins, Philipp Koehn, Ivona Kučerová*2005 · 918 citations*](https://md.research.elamur.ai/free/en/papers/collins-2005-clause-restructuring-statistical-machine-translation/)
4. [**BARTScore: Evaluating Generated Text as Text Generation**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
5. [**Large Language Models in Machine Translation**Thorsten Brants, Ashok C. Popat, Peng Xu, et al.*2007 · 701 citations*](https://md.research.elamur.ai/free/en/papers/brants-2007-large-language-models-machine-translation/)
6. [**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/)
7. [**Improving Machine Translation Performance by Exploiting Non-Parallel Corpora**Dragos Stefan Munteanu, Daniel Marcu*2005 · 576 citations*](https://md.research.elamur.ai/free/en/papers/munteanu-2005-improving-machine-translation-performance-exploiting/)
8. [**Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates**Taku Kudo*2018 · 561 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/)
9. [**A Simple and Effective Hierarchical Phrase Reordering Model**Michel Galley, Christopher D. Manning*2008 · 472 citations*](https://md.research.elamur.ai/free/en/papers/galley-2008-simple-effective-hierarchical-phrase-reordering/)
10. [**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/)
11. [**Manual and Automatic Evaluation of Machine Translation between European Languages**Philipp Koehn, Christof Monz*2006 · 433 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2006-manual-automatic-evaluation-machine-translation/)
12. [**Word Sense Disambiguation Improves Statistical Machine Translation**Yee Seng Chan, Hwee Tou Ng, David Chiang*2007 · 415 citations*](https://md.research.elamur.ai/free/en/papers/chan-2007-word-sense-disambiguation-improves-statistical/)
13. [**Are Sixteen Heads Really Better than One?**Paul Michel, Omer Levy, Graham Neubig*2019 · 412 citations*](https://md.research.elamur.ai/free/en/papers/michel-2019-sixteen-heads-really-better-than/)
14. [**Document-Level Neural Machine Translation with Hierarchical Attention Networks**Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, et al.*2018 · 411 citations*](https://md.research.elamur.ai/free/en/papers/miculicich-2018-document-level-neural-machine-translation/)
15. [**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/)
16. [**Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics**Nitika Mathur, Timothy Baldwin, Trevor Cohn*2020 · 385 citations*](https://md.research.elamur.ai/free/en/papers/mathur-2020-tangled-up-bleu-reevaluating-evaluation/)
17. [**Position-Aware Tagging for Aspect Sentiment Triplet Extraction**Lu Xu, Hao Li, Wei Lu, et al.*2020 · 370 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/)
18. [**Arabic Preprocessing Schemes for Statistical Machine Translation**Nizar Habash, Fatiha Sadat*2006 · 358 citations*](https://md.research.elamur.ai/free/en/papers/habash-2006-arabic-preprocessing-schemes-statistical-machine/)
19. [**Estimating the Sentence-Level Quality of Machine Translation Systems**Lucia Specia, Marco Turchi, Nicola Cancedda, et al.*2009 · 351 citations*](https://md.research.elamur.ai/free/en/papers/specia-2009-estimating-sentence-level-quality-machine/)
20. [**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/)
21. [**Simple English Wikipedia: A New Text Simplification Task**William Coster, David Kauchak*2011 · 321 citations*](https://md.research.elamur.ai/free/en/papers/coster-2011-simple-english-wikipedia-new-text/)
22. [**Paraphrasing for Automatic Evaluation**David Kauchak, Regina Barzilay*2006 · 321 citations*](https://md.research.elamur.ai/free/en/papers/kauchak-2006-paraphrasing-automatic-evaluation/)
23. [**On Some Pitfalls in Automatic Evaluation and Significance Testing for MT**Stefan Riezler, John T. Maxwell*2005 · 318 citations*](https://md.research.elamur.ai/free/en/papers/riezler-2005-some-pitfalls-automatic-evaluation-significance/)
24. [**An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation**Chenhui Chu, Raj Dabre, Sadao Kurohashi*2017 · 316 citations*](https://md.research.elamur.ai/free/en/papers/chu-2017-empirical-comparison-domain-adaptation-methods/)
25. [**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/)
26. [**Incorporating Discrete Translation Lexicons into Neural Machine Translation**Philip Arthur, Graham Neubig, Satoshi Nakamura*2016 · 298 citations*](https://md.research.elamur.ai/free/en/papers/arthur-2016-incorporating-discrete-translation-lexicons-into/)
27. [**BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/)
28. [**Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges**Qingsong Ma, Johnny Wei, Ondřej Bojar, et al.*2019 · 283 citations*](https://md.research.elamur.ai/free/en/papers/ma-2019-results-wmt19-metrics-shared-task/)
29. [**Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised**Stefanos Angelidis, Mirella Lapata*2018 · 265 citations*](https://md.research.elamur.ai/free/en/papers/angelidis-2018-summarizing-opinions-aspect-extraction-meets/)
30. [**Learning Global Features for Coreference Resolution**Sam Wiseman, Alexander M. Rush, Stuart M. Shieber*2016 · 264 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-coreference-resolution/)
31. [**Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing**Brian Thompson, Matt Post*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
32. [**Re-evaluating Evaluation in Text Summarization**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 256 citations*](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
33. [**Tower: An Open Multilingual Large Language Model for Translation-Related Tasks**Duarte M. Alves, Jose Pombal, Nuno M. Guerreiro, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/alves-2024-tower-open-multilingual-large-language/)
34. [**Learning to Parse and Translate Improves Neural Machine Translation**Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho*2017 · 214 citations*](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/)
35. [**Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases**Yuval Marton, Chris Callison-Burch, Philip Resnik*2009 · 213 citations*](https://md.research.elamur.ai/free/en/papers/marton-2009-improved-statistical-machine-translation-monolingually/)
36. [**Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution**Sam Wiseman, Alexander M. Rush, Stuart Shieber, et al.*2015 · 209 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2015-learning-anaphoricity-antecedent-ranking-features/)
37. [**Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE**Yvette Graham*2015 · 208 citations*](https://md.research.elamur.ai/free/en/papers/graham-2015-re-evaluating-automatic-summarization-bleu/)
38. [**Findings of the 2012 Workshop on Statistical Machine Translation**Chris Callison-Burch, Philipp Koehn, Christof Monz, et al.*2012 · 203 citations*](https://md.research.elamur.ai/free/en/papers/burch-2012-findings-2012-workshop-statistical-machine/)
39. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
40. [**Learning to Simplify Sentences Using Wikipedia**Will Coster, David Kauchak*2011 · 196 citations*](https://md.research.elamur.ai/free/en/papers/coster-2011-learning-simplify-sentences-wikipedia/)
41. [**Results of the WMT20 Metrics Shared Task**Nitika Mathur, Johnny Wei, Markus Freitag, et al.*2020 · 195 citations*](https://md.research.elamur.ai/free/en/papers/mathur-2020-results-wmt20-metrics-shared-task/)
42. [**Findings of the 2021 Conference on Machine Translation (WMT21)**Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, et al.*2021 · 189 citations*](https://md.research.elamur.ai/free/en/papers/akhbardeh-2021-findings-2021-conference-machine-translation/)
43. [**compare-mt: A Tool for Holistic Comparison of Language Generation Systems**Graham Neubig, Zi-Yi Dou, Junjie Hu, et al.*2019 · 185 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2019-compare-mt-tool-holistic-comparison/)
44. [**Investigating Backtranslation in Neural Machine Translation**Alberto Poncelas, Dimitar Shterionov, Andy Way, et al.*2018 · 184 citations*](https://md.research.elamur.ai/free/en/papers/poncelas-2018-investigating-backtranslation-neural-machine-translation/)
45. [**Soft Syntactic Constraints for Hierarchical Phrased-Based Translation**Yuval Marton, Philip Resnik*2008 · 183 citations*](https://md.research.elamur.ai/free/en/papers/marton-2008-soft-syntactic-constraints-hierarchical-phrased/)
46. [**Findings of the IWSLT 2022 Evaluation Campaign**Antonios Anastasopoulos, Loïc Barrault, Luisa Bentivogli, et al.*2022 · 177 citations*](https://md.research.elamur.ai/free/en/papers/anastasopoulos-2022-findings-iwslt-2022-evaluation-campaign/)
47. [**Results of the WMT18 Metrics Shared Task: Both characters and embeddings achieve good performance**Qingsong Ma, Ondřej Bojar, Yvette Graham*2018 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ma-2018-results-wmt18-metrics-shared-task/)
48. [**Semantic Neural Machine Translation Using AMR**Linfeng Song, Daniel Gildea, Yue Zhang, et al.*2019 · 159 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-semantic-neural-machine-translation-amr/)
49. [**Accurate Evaluation of Segment-level Machine Translation Metrics**Yvette Graham, Timothy Baldwin, Nitika Mathur*2015 · 155 citations*](https://md.research.elamur.ai/free/en/papers/graham-2015-accurate-evaluation-segment-level-machine/)
50. [**On The Evaluation of Machine Translation Systems Trained With Back-Translation**Sergey Edunov, Myle Ott, Marc'Aurelio Ranzato, et al.*2020 · 139 citations*](https://md.research.elamur.ai/free/en/papers/edunov-2020-evaluation-machine-translation-systems-trained/)
51. [**Trivial Transfer Learning for Low-Resource Neural Machine Translation**Tom Kocmi, Ondřej Bojar*2018 · 139 citations*](https://md.research.elamur.ai/free/en/papers/kocmi-2018-trivial-transfer-learning-low-resource/)
52. [**Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation**Eva Vanmassenhove, Dimitar Shterionov, Andy Way*2019 · 137 citations*](https://md.research.elamur.ai/free/en/papers/vanmassenhove-2019-lost-translation-loss-decay-linguistic/)
53. [**Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs**Cristian Danescu-Niculescu-Mizil, Lillian Lee*2011 · 110 citations*](https://md.research.elamur.ai/free/en/papers/mizil-2011-chameleons-imagined-conversations-new-approach/)
54. [**Findings of the WMT 2019 Shared Task on Automatic Post-Editing**Rajen Chatterjee, Christian Federmann, Matteo Negri, et al.*2019 · 102 citations*](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-findings-wmt-2019-shared-task/)
55. [**Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering**Rui Zhang, Cícero Nogueira dos Santos, Michihiro Yasunaga, et al.*2018 · 83 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-neural-coreference-resolution-deep-biaffine/)
56. [**Modality and Negation in SIMT Use of Modality and Negation in Semantically-Informed Syntactic MT**Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr, et al.*2012 · 77 citations*](https://md.research.elamur.ai/free/en/papers/baker-2012-modality-negation-simt-use-modality/)
57. [**CUNI Transformer Neural MT System for WMT18**Martin Popel*2018 · 61 citations*](https://md.research.elamur.ai/free/en/papers/popel-2018-cuni-transformer-neural-mt-system/)
58. [**Inferring Shallow-Transfer Machine Translation Rules from Small Parallel Corpora**Felipe Sanchez-Martinez, Mikel L. Forcada*2009 · 38 citations*](https://md.research.elamur.ai/free/en/papers/sanchez-martinez-2014-inferring-shallow-transfer-machine-translation/)
59. [**DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models**Shansan Gong, Mukai Li, Jiangtao Feng, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/gong-2023-diffuseq-v2-bridging-discrete-continuous/)
60. [**Incorporating Information Status into Generation Ranking**Aoife Cahill, Arndt Riester*2009 · 32 citations*](https://md.research.elamur.ai/free/en/papers/cahill-2009-incorporating-information-status-into-generation/)
61. [**Flexible finite-state lexical selection for rule-based machine translation**Francis M. Tyers, Felipe Sánchez-Martínez, Mikel L. Forcada*2012 · 31 citations*](https://md.research.elamur.ai/free/en/papers/tyers-2012-flexible-finite-state-lexical-selection/)
62. [**Unsupervised Morphology Rivals Supervised Morphology for Arabic MT**David Stallard, Jacob Devlin, Michael Kayser, et al.*2012 · 30 citations*](https://md.research.elamur.ai/free/en/papers/stallard-2012-unsupervised-morphology-rivals-supervised-morphology/)
63. [**Formemes in English-Czech Deep Syntactic MT**Ondřej Dušek, Zdeněk Žabokrtský, Martin Popel, et al.*2012 · 28 citations*](https://md.research.elamur.ai/free/en/papers/dusek-2012-formemes-english-czech-deep-syntactic/)
64. [**Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models**Daniel Torregrosa, Nivranshu Pasricha, Maraim Masoud, et al.*2019 · 20 citations*](https://md.research.elamur.ai/free/en/papers/torregrosa-2019-leveraging-rule-based-machine-translation/)
65. [**Spelling Normalization of Historical Documents by Using a Machine Translation Approach**Miguel Domingo, Francisco Casacuberta*2018 · 20 citations*](https://md.research.elamur.ai/free/en/papers/domingo-2018-spelling-normalization-historical-documents-machine/)
66. [**Hebrew Morphological Preprocessing for Statistical Machine Translation**Nimesh Singh, Nizar Habash*2012 · 15 citations*](https://md.research.elamur.ai/free/en/papers/singh-2012-hebrew-morphological-preprocessing-statistical-machine/)
67. [**GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning**Rita Ramos, Everlyn Asiko Chimoto, Maartje Ter Hoeve, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2025-grammamt-improving-machine-translation-grammar/)
68. [**CQE: A Comprehensive Quantity Extractor**Satya Almasian, Vivian Kazakova, Philipp Göldner, et al.*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/almasian-2023-cqe-comprehensive-quantity-extractor/)
69. [**LLM Dependency Parsing with In-Context Rules**Michael Ginn, Alexis Palmer*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/ginn-2025-llm-dependency-parsing-context-rules/)
70. [**Predicting and Using a Pragmatic Component of Lexical Aspect of Simple Past Verbal Tenses for Improving English-to-French Machine Translation**Sharid Loáiciga, Cristina Grisot*2016 · 5 citations*](https://md.research.elamur.ai/free/en/papers/loaiciga-2016-predicting-pragmatic-component-lexical-aspect/)
71. [**Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?**Aishwarya Ramasethu, Rohin Garg, Niyathi Allu, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ramasethu-2026-can-linguistically-related-languages-guide/)
72. [**Multilingual Communication in the Asylum Context: Evaluating LLM-Based Machine Translation with Fuzzy Match Augmentation and Adaptive NMT across Resource Conditions under Low-Data Constraints**Thomas Moerman, Arda Tezcan, Lieve Macken*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/moerman-2026-multilingual-communication-asylum-context-evaluating/)
73. [**Bringing Mapudungun into the Modern MT Ecosystem: Morphology-Aware Tokenization for NLLB-200 Fine-Tuning**Isaac Thompson, Brandon Rogers, Eric Ringger*2026*](https://md.research.elamur.ai/free/en/papers/thompson-2026-bringing-mapudungun-into-modern-mt/)
74. [**Corpora Generation for Urdu Grammatical Error Correction**Syed Ahad, Burhanuddin Aliasghar Ezzi, Muhammad Arsalan Hussain, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ahad-2026-corpora-generation-urdu-grammatical-error/)
75. [**Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite**Klaudia Thellmann, Bernhard Stadler, Michael Färber*2026*](https://md.research.elamur.ai/free/en/papers/thellmann-2026-diagnosing-translated-benchmarks-automated-quality/)
76. [**LV-ROVER-MLT: Low-Resource Maltese OCR by Synthetic Fine-Tuning and Multi-Stream Arbitration**Adam Darmanin*2026*](https://md.research.elamur.ai/free/en/papers/darmanin-2026-lv-rover-mlt-low-resource/)
77. [**Terminology-Aware Retrieval-Augmented Knowledge Distillation for Biomedical Neural Machine Translation**Maria Zafar, Souhail Bakkali, Rejwanul Haque*2026*](https://md.research.elamur.ai/free/en/papers/zafar-2026-terminology-aware-retrieval-augmented-knowledge/)
78. [**When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content**Lydia Nishimwe, Benoît Sagot, Rachel Bawden*2026*](https://md.research.elamur.ai/free/en/papers/nishimwe-2026-when-gold-standard-isn-t/)

971 more papers of our library cite it; they have no page here yet.

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