| Challenge: | Existing datasets for computer vision to distinguish printed or handwritten characters in digital images are limited to one language. |
| Approach: | They propose to use a Cyrillic version of the MNIST dataset to analyze handwritten letters. |
| Outcome: | The proposed dataset is compared to the Extended MNIST (EMNIST) dataset and is available at https://github.com/bolattleubayev/cmnist. |
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| Challenge: | a new resource is created to evaluate grammatical error correction models in English . a subset of the dataset is annotated in Russian, which is hard to come by and expensive to annotate . |
| Approach: | They develop an annotated learner corpus of Russian extracted from the Lang-8 website. |
| Outcome: | The proposed dataset is compared against two state-of-the-art grammatical error correction models . the results show that the created corpus is more diverse than the existing one . |
Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)
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| Challenge: | Revita Learner Corpus is a semi-automatically annotated learner corpus for Russian . it is used for research in second language acquisition and foreign language teaching . |
| Approach: | They propose a semi-automatically annotated learner corpus for Russian that detects errors automatically and annotates errors by type. |
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Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
| Approach: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . call for papers for this second workshop met with a strong response . |
| Outcome: | the EMNLP-IJCNLP 2019 workshop on deep learning approaches for low-resource natural language processing takes place in Hong Kong, China. |
StRuCom: A Novel Dataset of Structured Code Comments in Russian (2025.acl-srw)
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| Challenge: | Existing machine learning models for code comment generation are poorly suited for Russian . existing datasets that contain simple comments and docstrings in English are not suitable for function-level documentation generation. |
| Approach: | They propose a dataset specifically designed for Russian code documentation. |
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Findings of the Association for Computational Linguistics: EMNLP 2022 (2022.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |
Towards Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)
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| Challenge: | a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator . |
| Approach: | They propose to augment a neural machine translation model with translator information . they use TED talk datasets to model and control translator-related stylistic variations . |
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A Multi-dimensional Evaluation of Tokenizer-free Multilingual Pretrained Models (2023.findings-eacl)
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| Challenge: | Recent work on tokenizer-free models shows promising results in cross-lingual transfer . previous work focused on reporting accuracy on a limited set of tasks and data settings . |
| Approach: | They compare tokenizer-free and subword-based models using various dimensions . they find subword models are still the most practical choice in many settings . |
| Outcome: | The proposed model improves cross-lingual transfer and reduces engineering overhead. |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
Findings of the Association for Computational Linguistics: EMNLP 2025 (2025.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |