---
title: "Who cites Charagram: Embedding Words — 73 citing papers"
description: "73 papers of the Elamur Research library cite «Charagram: Embedding Words and Sentences via Character n-grams», found in their…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Charagram: Embedding Words and Sentences via…](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/) / cited by

# Who cites «Charagram: Embedding Words and Sentences via Character n-grams»

73 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Charagram: Embedding Words and Sentences via Character n-grams»](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/)

1. **[Deep Contextualized Word Representations](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 14,852 citations*
2. **[Enriching Word Vectors with Subword Information](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Piotr Bojanowski, Edouard Grave, Armand Joulin, et al.*2017 · 11,848 citations*
3. **[SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 1,813 citations*
4. **[SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alexis Conneau, Douwe Kiela*2018 · 743 citations*
5. **[A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://md.research.elamur.ai/free/en/papers/hashimoto-2016-joint-many-task-model-growing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, et al.*2016 · 668 citations*
6. **[Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 297 citations*
7. **[Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints](https://md.research.elamur.ai/free/en/papers/mrksic-2017-semantic-specialization-distributional-word-vector/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nikola Mrkšić, Ivan Vulić, Diarmuid Ó Séaghdha, et al.*2017 · 236 citations*
8. [**Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings**Pamela Shapiro, Kevin Duh*2018 · 25 citations*](https://md.research.elamur.ai/free/en/papers/shapiro-2018-morphological-word-embeddings-arabic-neural/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/) — Charagram: Embedding Words and Sentences via Character n-grams