---
title: "Who cites Enriching Word Vectors with Subword Information"
description: "2,218 papers of the Elamur Research library cite «Enriching Word Vectors with Subword Information», found in their bibliographies · page 1 of 2 · 1–100…"
canonical: "https://research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Enriching Word Vectors with Subword Information](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/) / cited by

# Who cites «Enriching Word Vectors with Subword Information»

2,218 papers of our library cite it (of the 140,884 whose bibliographies we read) · 157 with a page here, the most cited first · page 1 of 2 · 1–100 · [← the paper](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/)

**1** [2](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/page/2/)

1. [**Deep Contextualized Word Representations**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 3,240 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/)
2. [**Stanza: A Python Natural Language Processing Toolkit for Many Human Languages**Peng Qi, Yuhao Zhang, Yuhui Zhang, et al.*2020 · 2,347 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-stanza-python-natural-language-processing/)
3. [**Pre-trained Models for Natural Language Processing: A Survey**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,740 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
4. [**Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects**Jianmo Ni, Jiacheng Li, Julian McAuley*2019 · 1,421 citations*](https://md.research.elamur.ai/free/en/papers/ni-2019-justifying-recommendations-distantly-labeled-reviews/)
5. [**AraBERT: Transformer-based Model for Arabic Language Understanding**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,297 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
6. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
7. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
8. [**Publicly Available Clinical BERT Embeddings**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,209 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
9. [**Predicting the Type and Target of Offensive Posts in Social Media**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-predicting-type-target-offensive-posts/)
10. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
11. [**TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification**Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, et al.*2020 · 952 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2020-tweeteval-unified-benchmark-comparative-evaluation/)
12. [**Patient Knowledge Distillation for BERT Model Compression**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 939 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
13. [**Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features**Matteo Pagliardini, Prakhar Gupta, Martin Jaggi*2018 · 840 citations*](https://md.research.elamur.ai/free/en/papers/pagliardini-2018-unsupervised-learning-sentence-embeddings-compositional/)
14. [**DocVQA: A Dataset for VQA on Document Images**Minesh Mathew, Dimosthenis Karatzas, C.V. Jawahar*2020 · 820 citations*](https://md.research.elamur.ai/free/en/papers/mathew-2020-docvqa-dataset-vqa-document-images/)
15. [**ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission**Kexin Huang, Jaan Altosaar, Rajesh Ranganath*2019 · 788 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-clinicalbert-modeling-clinical-notes-predicting/)
16. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
17. [**SPECTER: Document-level Representation Learning using Citation-informed Transformers**Arman Cohan, Sergey Feldman, Iz Beltagy, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2020-specter-document-level-representation-learning/)
18. [**ELI5: Long Form Question Answering**Angela Fan, Yacine Jernite, Ethan Perez, et al.*2019 · 682 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/)
19. [**A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks**Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, et al.*2016 · 666 citations*](https://md.research.elamur.ai/free/en/papers/hashimoto-2016-joint-many-task-model-growing/)
20. [**Unsupervised Machine Translation Using Monolingual Corpora Only**Guillaume Lample, Alexis Conneau, Ludovic Denoyer, et al.*2017 · 650 citations*](https://md.research.elamur.ai/free/en/papers/lample-2017-unsupervised-machine-translation-using-monolingual/)
21. [**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/)
22. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
23. [**Learning Word Vectors for 157 Languages**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 640 citations*](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/)
24. [**Advances in Pre-Training Distributed Word Representations**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 614 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/)
25. [**Named Entity Recognition as Dependency Parsing**Juntao Yu, Bernd Bohnet, Massimo Poesio*2020 · 594 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-named-entity-recognition-as-dependency/)
26. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
27. [**ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection**Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-icon-interactive-conversational-memory-network/)
28. [**Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction**Hu Xu, Bing Liu, Lei Shu, et al.*2018 · 494 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-double-embeddings-cnn-based-sequence/)
29. [**Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion**Armand Joulin, Piotr Bojanowski, Tomas Mikolov, et al.*2018 · 477 citations*](https://md.research.elamur.ai/free/en/papers/joulin-2018-loss-translation-learning-bilingual-word/)
30. [**When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?**Ye Qi, Devendra Sachan, Matthieu Felix, et al.*2018 · 466 citations*](https://md.research.elamur.ai/free/en/papers/qi-2018-when-why-are-pre-trained/)
31. [**Reducing Gender Bias in Abusive Language Detection**Ji Ho Park, Jamin Shin, Pascale Fung*2018 · 464 citations*](https://md.research.elamur.ai/free/en/papers/park-2018-reducing-gender-bias-abusive-language/)
32. [**Are All Languages Created Equal in Multilingual BERT?**Shijie Wu, Mark Dredze*2020 · 458 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
33. [**Meta-Learning for Low-Resource Neural Machine Translation**Jiatao Gu, Yong Wang, Yun Chen, et al.*2018 · 451 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/)
34. [**CARER: Contextualized Affect Representations for Emotion Recognition**Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, et al.*2018 · 448 citations*](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/)
35. [**HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization**Xingxing Zhang, Furu Wei, Ming Zhou*2019 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)
36. [**Neural Architectures for Nested NER through Linearization**Jana Straková, Milan Straka, Jan Hajic*2019 · 400 citations*](https://md.research.elamur.ai/free/en/papers/strakova-2019-neural-architectures-nested-ner-through/)
37. [**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/)
38. [**On the Limitations of Unsupervised Bilingual Dictionary Induction**Anders Søgaard, Sebastian Ruder, Ivan Vulić*2018 · 396 citations*](https://md.research.elamur.ai/free/en/papers/sogaard-2018-limitations-unsupervised-bilingual-dictionary-induction/)
39. [**Pooled Contextualized Embeddings for Named Entity Recognition**Alan Akbik, Tanja Bergmann, Roland Vollgraf*2019 · 385 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-pooled-contextualized-embeddings-named-entity/)
40. [**Emerging Cross-lingual Structure in Pretrained Language Models**Alexis Conneau, Shijie Wu, Haoran Li, et al.*2020 · 378 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/)
41. [**JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages**Željko Agić, Ivan Vulić*2019 · 373 citations*](https://md.research.elamur.ai/free/en/papers/agic-2019-jw300-wide-coverage-parallel-corpus/)
42. [**Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good**Xuewei Wang, Weiyan Shi, Richard Kim, et al.*2019 · 366 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-persuasion-good-personalized-persuasive-dialogue/)
43. [**The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English**Francisco Guzmán, Peng-Jen Chen, Myle Ott, et al.*2019 · 361 citations*](https://md.research.elamur.ai/free/en/papers/guzman-2019-flores-evaluation-datasets-low-resource/)
44. [**Multilingual Constituency Parsing with Self-Attention and Pre-Training**Nikita Kitaev, Steven Cao, Dan Klein*2019 · 356 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2019-multilingual-constituency-parsing-self-attention/)
45. [**Universal Neural Machine Translation for Extremely Low Resource Languages**Jiatao Gu, Hany Hassan, Jacob Devlin, et al.*2018 · 356 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/)
46. [**Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task**Timothy Dozat, Peng Qi, Christopher D. Manning*2017 · 346 citations*](https://md.research.elamur.ai/free/en/papers/dozat-2017-stanford-s-graph-based-neural/)
47. [**Lessons from Natural Language Inference in the Clinical Domain**Alexey Romanov, Chaitanya Shivade*2018 · 343 citations*](https://md.research.elamur.ai/free/en/papers/romanov-2018-lessons-natural-language-inference-clinical/)
48. [**Massively Multilingual Transfer for NER**Afshin Rahimi, Yuan Li, Trevor Cohn*2019 · 328 citations*](https://md.research.elamur.ai/free/en/papers/rahimi-2019-massively-multilingual-transfer-ner/)
49. [**Poincaré Embeddings for Learning Hierarchical Representations**Maximilian Nickel, Douwe Kiela*2017 · 324 citations*](https://md.research.elamur.ai/free/en/papers/nickel-2017-poincare-embeddings-learning-hierarchical-representations/)
50. [**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/)
51. [**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/)
52. [**Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing**Tal Schuster, Ori Ram, Regina Barzilay, et al.*2019 · 294 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-alignment-contextual-word/)
53. [**Information-Theoretic Probing for Linguistic Structure**Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, et al.*2020 · 293 citations*](https://md.research.elamur.ai/free/en/papers/pimentel-2020-information-theoretic-probing-linguistic-structure/)
54. [**Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/)
55. [**The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants**Lucas Bandarkar, Davis Liang, Benjamin Muller, et al.*2023 · 259 citations*](https://md.research.elamur.ai/free/en/papers/bandarkar-2023-belebele-benchmark-parallel-reading-comprehension/)
56. [**Towards Debiasing Sentence Representations**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/)
57. [**UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification**Andreas Hanselowski, Hao Zhang, Zile Li, et al.*2018 · 258 citations*](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-ukp-athene-multi-sentence-textual/)
58. [**Multilingual is not enough: BERT for Finnish**Antti Virtanen, Jenna Kanerva, Rami Ilo, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-multilingual-enough-bert-finnish/)
59. [**BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages**Benjamin Heinzerling, Michael Strube*2018 · 256 citations*](https://md.research.elamur.ai/free/en/papers/heinzerling-2018-bpemb-tokenization-free-pre-trained/)
60. [**The Multilingual Amazon Reviews Corpus**Phillip Keung, Yichao Lu, György Szarvas, et al.*2020 · 255 citations*](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/)
61. [**Survey of Low-Resource Machine Translation**Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.*2022 · 251 citations*](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
62. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
63. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
64. [**Automatic Detection of Machine Generated Text: A Critical Survey**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/)
65. [**A review of sentiment analysis research in Arabic language**Oumaima Oueslati; Erik Cambria; Moez Ben HajHmida;, Habib Ounelli*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/oueslati-2020-review-sentiment-analysis-research-arabic/)
66. [**Pretrained Language Models for Biomedical and Clinical Tasks: Understanding and Extending the State-of-the-Art**Patrick Lewis, Myle Ott, Jingfei Du, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-pretrained-language-models-biomedical-clinical/)
67. [**SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets**Parth Patwa, Gustavo Aguilar, Sudipta Kar, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/patwa-2020-semeval-2020-task-9-overview/)
68. [**AlBERTo: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets**Marco Polignano, Pierpaolo Basile, Marco de Gemmis, et al.*2019 · 228 citations*](https://md.research.elamur.ai/free/en/papers/polignano-2019-alberto-italian-bert-language-understanding/)
69. [**A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages**Pedro Javier Ortiz Suárez, Laurent Romary, Benoît Sagot*2020 · 219 citations*](https://md.research.elamur.ai/free/en/papers/suarez-2020-monolingual-approach-contextualized-word-embeddings/)
70. [**Unsupervised Question Decomposition for Question Answering**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/)
71. [**ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations**Fernando Alva-Manchego, Louis Martin, Antoine Bordes, et al.*2020 · 215 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2020-asset-dataset-tuning-evaluation-sentence/)
72. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
73. [**Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation**Daniel Loureiro, Alípio Jorge*2019 · 189 citations*](https://md.research.elamur.ai/free/en/papers/loureiro-2019-language-modelling-makes-sense-propagating/)
74. [**Modeling Empathy and Distress in Reaction to News Stories**Sven Buechel, Anneke Buffone, Barry Slaff, et al.*2018 · 181 citations*](https://md.research.elamur.ai/free/en/papers/buechel-2018-modeling-empathy-distress-reaction-news/)
75. [**Eliciting Latent Predictions from Transformers with the Tuned Lens**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
76. [**AudioCaps: Generating Captions for Audios in The Wild**Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, et al.*2019 · 176 citations*](https://md.research.elamur.ai/free/en/papers/kim-2019-audiocaps-generating-captions-audios-wild/)
77. [**SemEval-2021 Task 5: Toxic Spans Detection**John Pavlopoulos, Jeffrey Sorensen, Léo Laugier, et al.*2021 · 172 citations*](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2021-semeval-2021-task-5-toxic/)
78. [**Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing**Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2021-trankit-light-weight-transformer-based/)
79. [**Arabic Offensive Language on Twitter: Analysis and Experiments**Hamdy Mubarak, Ammar Rashed, Kareem Darwish, et al.*2021 · 165 citations*](https://md.research.elamur.ai/free/en/papers/mubarak-2021-arabic-offensive-language-twitter-analysis/)
80. [**AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages**Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, et al.*2022 · 163 citations*](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/)
81. [**Tuning Multilingual Transformers for Language-Specific Named Entity Recognition**Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, et al.*2019 · 163 citations*](https://md.research.elamur.ai/free/en/papers/arkhipov-2019-tuning-multilingual-transformers-language-specific/)
82. [**QuALITY: Question Answering with Long Input Texts, Yes!**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/)
83. [**A Sentiment Analysis Dataset for Code-Mixed Malayalam-English**Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, et al.*2020 · 158 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-sentiment-analysis-dataset-code-mixed/)
84. [**Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 156 citations*](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/)
85. [**Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets**Paula Fortuna, Juan Soler, Leo Wanner*2020 · 156 citations*](https://md.research.elamur.ai/free/en/papers/fortuna-2020-toxic-hateful-offensive-or-abusive/)
86. [**Towards a Cleaner Document-Oriented Multilingual Crawled Corpus**Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, et al.*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/abadji-2022-towards-cleaner-document-oriented-multilingual/)
87. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
88. [**Arabic aspect based sentiment analysis using bidirectional GRU based models**Mohammed M.Abdelgwad, Taysir Hassan A Soliman, Ahmed I.Taloba, et al.*2021 · 127 citations*](https://md.research.elamur.ai/free/en/papers/m-abdelgwad-2021-arabic-aspect-based-sentiment-analysis/)
89. [**Knowledge Enhanced Masked Language Model for Stance Detection**Kornraphop Kawintiranon, Lisa Singh*2021 · 119 citations*](https://md.research.elamur.ai/free/en/papers/kawintiranon-2021-knowledge-enhanced-masked-language-model/)
90. [**Intrinsic Bias Metrics Do Not Correlate with Application Bias**Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, et al.*2021 · 116 citations*](https://md.research.elamur.ai/free/en/papers/tarrant-2021-intrinsic-bias-metrics-do-not/)
91. [**Multi-Task Stance Detection with Sentiment and Stance Lexicons**Yingjie Li, Cornelia Caragea*2019 · 113 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-multi-task-stance-detection-sentiment/)
92. [**LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts**Don Tuggener, Pius von Däniken, Thomas Peetz, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/tuggener-2020-ledgar-large-scale-multi-label/)
93. [**Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words**Valentin, Hofmann*2021 · 101 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2021-superbizarre-derivational/)
94. [**WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models**Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz*2022 · 90 citations*](https://md.research.elamur.ai/free/en/papers/minixhofer-2022-wechsel-effective-initialization-subword-embeddings/)
95. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 89 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
96. [**CASCADE: Contextual Sarcasm Detection in Online Discussion Forums**Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, et al.*2018 · 89 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-cascade-contextual-sarcasm-detection-online/)
97. [**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/)
98. [**Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT**Mustafa Jarrar, Mohammed Khalilia, Sana Ghanem*2022 · 73 citations*](https://md.research.elamur.ai/free/en/papers/jarrar-2022-wojood-nested-arabic-named-entity/)
99. [**MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology**Khuyagbaatar Batsuren, Gabor Bella, Fausto Giunchiglia*2021 · 56 citations*](https://md.research.elamur.ai/free/en/papers/batsuren-2021-morphynet/)
100. [**Target-Aware Data Augmentation for Stance Detection**Yingjie Li, Cornelia Caragea*2021 · 56 citations*](https://md.research.elamur.ai/free/en/papers/li-2021-target-aware-data-augmentation-stance/)

**1** [2](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/) — Enriching Word Vectors with Subword Information