---
title: "Who cites A Study of Translation Edit Rate with Targeted…"
description: "1,514 papers of the Elamur Research library cite «A Study of Translation Edit Rate with Targeted Human Annotation», found in their bibliographies · page 1…"
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# Who cites «A Study of Translation Edit Rate with Targeted Human Annotation»

1,514 papers of our library cite it (of the 140,884 whose bibliographies we read) · 112 with a page here, the most cited first · page 1 of 2 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/)

**1** [2](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/cited-by/page/2/)

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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. [**Findings of the 2014 Workshop on Statistical Machine Translation**Ondřej Bojar, Christian Buck, Christian Federmann, et al.*2014 · 923 citations*](https://md.research.elamur.ai/free/en/papers/bojar-2014-findings-2014-workshop-statistical-machine/)
4. [**SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 907 citations*](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/)
5. [**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/)
6. [**Achieving Human Parity on Automatic Chinese to English News Translation**Hany Hassan, Anthony Aue, Chang Chen, et al.*2018 · 794 citations*](https://md.research.elamur.ai/free/en/papers/hassan-2018-achieving-human-parity-automatic-chinese/)
7. [**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/)
8. [***SEM 2013 shared task: Semantic Textual Similarity**Eneko Agirre, Daniel Cer, Mona Diab, et al.*2013 · 658 citations*](https://md.research.elamur.ai/free/en/papers/agirre-2013-sem-2013-shared-task-semantic/)
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13. [**Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems**Michael Denkowski, Alon Lavie*2011 · 541 citations*](https://md.research.elamur.ai/free/en/papers/denkowski-2011-meteor-1-3-automatic-metric/)
14. [**Improving Statistical Machine Translation Using Word Sense Disambiguation**Marine Carpuat, Dekai Wu*2007 · 505 citations*](https://md.research.elamur.ai/free/en/papers/carpuat-2007-improving-statistical-machine-translation-word/)
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. [**Automatic Evaluation of Translation Quality for Distant Language Pairs**Hideki Isozaki, Tsutomu Hirao, Kevin Duh, et al.*2010 · 461 citations*](https://md.research.elamur.ai/free/en/papers/isozaki-2010-automatic-evaluation-translation-quality-distant/)
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. [**Tuning as Ranking**Mark Hopkins, Jonathan May*2011 · 391 citations*](https://md.research.elamur.ai/free/en/papers/hopkins-2011-tuning-as-ranking/)
20. [**METEOR: An Automatic Metric for MT Evaluation with High Levels of Correlation with Human Judgments**Alon Lavie, Abhaya Agarwal*2007 · 387 citations*](https://md.research.elamur.ai/free/en/papers/lavie-2007-meteor-automatic-metric-mt-evaluation/)
21. [**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/)
22. [**A New String-to-Dependency Machine Translation Algorithm with a Target Dependency Language Model**Libin Shen, Jinxi Xu, Ralph Weischedel*2008 · 382 citations*](https://md.research.elamur.ai/free/en/papers/shen-2008-new-string-dependency-machine-translation/)
23. [**Crowdsourcing Translation: Professional Quality from Non-Professionals**Omar F. Zaidan, Chris Callison-Burch*2011 · 374 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2011-crowdsourcing-translation-professional-quality-non/)
24. [**cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models**Chris Dyer, Adam Lopez, Juri Ganitkevitch, et al.*2010 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dyer-2010-cdec-decoder-alignment-learning-framework/)
25. [**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/)
26. [**Neural Paraphrase Generation with Stacked Residual LSTM Networks**Aaditya Prakash, Sadid A. Hasan, Kathy Lee, et al.*2016 · 335 citations*](https://md.research.elamur.ai/free/en/papers/prakash-2016-neural-paraphrase-generation-stacked-residual/)
27. [**xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection**Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, et al.*2024 · 333 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2024-xcomet-transparent-machine-translation-evaluation/)
28. [**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/)
29. [**CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task**Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, et al.*2022 · 325 citations*](https://md.research.elamur.ai/free/en/papers/rei-2022-cometkiwi-ist-unbabel-2022-submission/)
30. [**Document-Level Machine Translation with Large Language Models**Longyue Wang, Chenyang Lyu, Tianbo Ji, et al.*2023 · 295 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-document-level-machine-translation-large/)
31. [**Why We Need New Evaluation Metrics for NLG**Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, et al.*2017 · 287 citations*](https://md.research.elamur.ai/free/en/papers/novikova-2017-why-we-need-new-evaluation/)
32. [**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/)
33. [**Re-examining Machine Translation Metrics for Paraphrase Identification**Nitin Madnani, Joel Tetreault, Martin Chodorow*2012 · 271 citations*](https://md.research.elamur.ai/free/en/papers/madnani-2012-re-examining-machine-translation-metrics/)
34. [**Statistical Phrase-Based Post-Editing**Michel Simard, Cyril Goutte, Pierre Isabelle*2007 · 270 citations*](https://md.research.elamur.ai/free/en/papers/simard-2007-statistical-phrase-based-post-editing/)
35. [**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/)
36. [**A Deep Generative Framework for Paraphrase Generation**Ankush Gupta, Arvind Agarwal, Prawaan Singh, et al.*2017 · 231 citations*](https://md.research.elamur.ai/free/en/papers/gupta-2017-deep-generative-framework-paraphrase-generation/)
37. [**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/)
38. [**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/)
39. [**BLEU might be Guilty but References are not Innocent**Markus Freitag, David Grangier, Isaac Caswell*2020 · 223 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2020-bleu-might-be-guilty-but/)
40. [**Data-Driven Sentence Simplification: Survey and Benchmark**Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2020-data-driven-sentence-simplification-survey/)
41. [**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/)
42. [**PET: a Tool for Post-editing and Assessing Machine Translation**Wilker Aziz, Sheila Castilho, Lucia Specia*2012 · 206 citations*](https://md.research.elamur.ai/free/en/papers/aziz-2012-pet-tool-post-editing-assessing/)
43. [**(Meta-) Evaluation of Machine Translation**Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, et al.*2007 · 206 citations*](https://md.research.elamur.ai/free/en/papers/burch-2007-meta-evaluation-machine-translation/)
44. [**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/)
45. [**Report on the 11th IWSLT evaluation campaign**Mauro Cettolo, Jan Niehues, Sebastian Stüker, et al.*2014 · 193 citations*](https://md.research.elamur.ai/free/en/papers/cettolo-2014-report-11th-iwslt-evaluation-campaign/)
46. [**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/)
47. [**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/)
48. [**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/)
49. [**Results of the WMT14 Metrics Shared Task**Matouš Macháček, Ondřej Bojar*2014 · 174 citations*](https://md.research.elamur.ai/free/en/papers/machacek-2014-results-wmt14-metrics-shared-task/)
50. [**The First Surface Realisation Shared Task: Overview and Evaluation Results**Anja Belz, Michael White, Dominic Espinosa, et al.*2011 · 172 citations*](https://md.research.elamur.ai/free/en/papers/belz-2011-first-surface-realisation-shared-task/)
51. [**Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain**Markus Freitag, Ricardo Rei, Nitika Mathur, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2021-results-wmt21-metrics-shared-task/)
52. [**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/)
53. [**The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)**Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, et al.*2020 · 163 citations*](https://md.research.elamur.ai/free/en/papers/ferreira-2020-2020-bilingual-bi-directional-webnlg/)
54. [**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/)
55. [**SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation**Xutai Ma, Mohammad Javad Dousti, Changhan Wang, et al.*2020 · 144 citations*](https://md.research.elamur.ai/free/en/papers/ma-2020-simuleval-evaluation-toolkit-simultaneous-translation/)
56. [**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/)
57. [**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/)
58. [**Findings of the 2009 Workshop on Statistical Machine Translation**Chris Callison-Burch, Philipp Koehn, Christof Monz, et al.*2009 · 136 citations*](https://md.research.elamur.ai/free/en/papers/burch-2009-findings-2009-workshop-statistical-machine/)
59. [**Findings of the 2011 Workshop on Statistical Machine Translation**Chris Callison-Burch, Philipp Koehn, Christof Monz, et al.*2011 · 135 citations*](https://md.research.elamur.ai/free/en/papers/burch-2011-findings-2011-workshop-statistical-machine/)
60. [**MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles**Chi-kiu Lo, Dekai Wu*2011 · 134 citations*](https://md.research.elamur.ai/free/en/papers/lo-2011-meant-inexpensive-high-accuracy-semi/)
61. [**Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description**Desmond Elliott, Stella Frank, Loïc Barrault, et al.*2017 · 133 citations*](https://md.research.elamur.ai/free/en/papers/elliott-2017-findings-second-shared-task-multimodal/)
62. [**Findings of the WMT 2023 Shared Task on Quality Estimation**Frederic Blain, Chrysoula Zerva, Ricardo Rei, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/blain-2023-findings-wmt-2023-shared-task/)
63. [**INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback**Wenda Xu, Danqing Wang, Liangming Pan, et al.*2023 · 122 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-instructscore-explainable-text-generation-evaluation/)
64. [**Fluency, Adequacy, or HTER? Exploring Different Human Judgments with a Tunable MT Metric**Matthew Snover, Nitin Madnani, Bonnie Dorr, et al.*2009 · 115 citations*](https://md.research.elamur.ai/free/en/papers/snover-2009-fluency-adequacy-or-hter-exploring/)
65. [**Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk**Chris Callison-Burch*2009 · 112 citations*](https://md.research.elamur.ai/free/en/papers/burch-2009-fast-cheap-creative-evaluating-translation/)
66. [**Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs**Fernando Alva-Manchego, Joachim Bingel, Gustavo Paetzold, et al.*2017 · 104 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2017-learning-how-simplify-explicit-labeling/)
67. [**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/)
68. [**UniTE: Unified Translation Evaluation**Yu Wan, Dayiheng Liu, Baosong Yang, et al.*2022 · 100 citations*](https://md.research.elamur.ai/free/en/papers/wan-2022-unite-unified-translation-evaluation/)
69. [**Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus**Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, et al.*2020 · 100 citations*](https://md.research.elamur.ai/free/en/papers/bentivogli-2020-gender-danger-evaluating-speech-translation/)
70. [**Unbabel's Participation in the WMT20 Metrics Shared Task**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 98 citations*](https://md.research.elamur.ai/free/en/papers/rei-2020-unbabel-s-participation-wmt20-metrics/)
71. [**Findings of the WMT 2022 Shared Task on Quality Estimation**Chrysoula Zerva, Frédéric Blain, Ricardo Rei, et al.*2022 · 91 citations*](https://md.research.elamur.ai/free/en/papers/zerva-2022-findings-wmt-2022-shared-task/)
72. [**Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing**Marcin Junczys-Dowmunt, Roman Grundkiewicz*2016 · 86 citations*](https://md.research.elamur.ai/free/en/papers/dowmunt-2016-log-linear-combinations-monolingual-bilingual/)
73. [**BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation**Yuchen Jiang, Tianyu Liu, Shuming Ma, et al.*2022 · 85 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-blonde-automatic-evaluation-metric-document/)
74. [**MEANT 2.0: Accurate semantic MT evaluation for any output language**Chi-kiu Lo*2017 · 81 citations*](https://md.research.elamur.ai/free/en/papers/lo-2017-meant-2-0-accurate-semantic/)
75. [**Findings of the WMT 2021 Shared Task on Quality Estimation**Lucia Specia, Frédéric Blain, Marina Fomicheva, et al.*2021 · 80 citations*](https://md.research.elamur.ai/free/en/papers/specia-2021-findings-wmt-2021-shared-task/)
76. [**Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric**Giorgos Vernikos, Brian Thompson, Prashant Mathur, et al.*2022 · 68 citations*](https://md.research.elamur.ai/free/en/papers/vernikos-2022-embarrassingly-easy-document-level-mt/)
77. [**TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties**Karima Kadaoui, Samar M. Magdy, Abdul Waheed, et al.*2023 · 60 citations*](https://md.research.elamur.ai/free/en/papers/kadaoui-2023-tarjamat-evaluation-bard-chatgpt-machine/)
78. [**HUME: Human UCCA-Based Evaluation of Machine Translation**Alexandra Birch, Omri Abend, Ondřej Bojar, et al.*2016 · 58 citations*](https://md.research.elamur.ai/free/en/papers/birch-2016-hume-human-ucca-based-evaluation/)
79. [**Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation**Thiago Castro Ferreira, Iacer Calixto, Sander Wubben, et al.*2017 · 46 citations*](https://md.research.elamur.ai/free/en/papers/ferreira-2017-linguistic-realisation-as-machine-translation/)
80. [**Taking MT Evaluation Metrics to Extremes: Beyond Correlation with Human Judgments**Marina Fomicheva, Lucia Specia*2019 · 44 citations*](https://md.research.elamur.ai/free/en/papers/fomicheva-2019-taking-mt-evaluation-metrics-extremes/)
81. [**Discourse Structure in Machine Translation Evaluation**Shafiq Joty, Francisco Guzmán, Lluís Màrquez, et al.*2017 · 40 citations*](https://md.research.elamur.ai/free/en/papers/joty-2017-discourse-structure-machine-translation-evaluation/)
82. [**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/)
83. [**Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains**Vilém Zouhar, Shuoyang Ding, Anna Currey, et al.*2024 · 32 citations*](https://md.research.elamur.ai/free/en/papers/zouhar-2024-fine-tuned-machine-translation-metrics/)
84. [**MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem**Stefano Perrella, Lorenzo Proietti, Alessandro Scirè, et al.*2022 · 32 citations*](https://md.research.elamur.ai/free/en/papers/perrella-2022-matese-machine-translation-evaluation-as/)
85. [**RED: A Reference Dependency Based MT Evaluation Metric**Hui Yu, Xiaofeng Wu, Jun Xie, et al.*2014 · 28 citations*](https://md.research.elamur.ai/free/en/papers/yu-2014-red-reference-dependency-based-mt/)
86. [**A Human Evaluation of AMR-to-English Generation Systems**Emma Manning, Shira Wein, Nathan Schneider*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/manning-2020-human-evaluation-amr-english-generation/)
87. [**Findings of WMT 2024 Shared Task on Low-Resource Indic Languages Translation**Partha Pakray, Santanu Pal, Advaitha Vetagiri, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/pakray-2024-findings-wmt-2024-shared-task/)
88. [**KoBE: Knowledge-Based Machine Translation Evaluation**Zorik Gekhman, Roee Aharoni, Genady Beryozkin, et al.*2020 · 20 citations*](https://md.research.elamur.ai/free/en/papers/gekhman-2020-kobe-knowledge-based-machine-translation/)
89. [**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/)
90. [**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/)
91. [**Assessing the Cross-linguistic Utility of Abstract Meaning Representation**Shira Wein, Nathan Schneider*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wein-2024-assessing-cross-linguistic-utility-abstract/)
92. [**Towards Heterogeneous Automatic MT Error Analysis**Jesús Giménez, Lluís Màrquez*2008 · 13 citations*](https://md.research.elamur.ai/free/en/papers/gimenez-2008-heterogeneous-automatic-mt-error-analysis/)
93. [**Referenceless Parsing-Based Evaluation of AMR-to-English Generation**Emma Manning, Nathan Schneider*2021 · 9 citations*](https://md.research.elamur.ai/free/en/papers/manning-2021-referenceless-parsing-based-evaluation-amr/)
94. [**DTED: Evaluation of Machine Translation Structure Using Dependency Parsing and Tree Edit Distance**Martin McCaffery, Mark-Jan Nederhof*2016 · 9 citations*](https://md.research.elamur.ai/free/en/papers/mccaffery-2016-dted-evaluation-machine-translation-structure/)
95. [**Assessing the Role of Context in Chat Translation Evaluation: Is Context Helpful and Under What Conditions?**Sweta Agrawal, Amin Farajian, Patrick Fernandes, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2024-assessing-role-context-chat-translation/)
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100. [**A Partially Rule-Based Approach to AMR Generation**Emma Manning*2019 · 2 citations*](https://md.research.elamur.ai/free/en/papers/manning-2019-partially-rule-based-approach-amr/)

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