Papers with Kazakh
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation (2021.eacl-srw)
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| Challenge: | Current NMT systems typically operate at the level of subwords, causing problems of vocabulary sparsity. |
| Approach: | They compare subword segmentation methods with morphologically-based methods in a low-resource setting . they find that no consistent and reliable differences emerge between the methods . |
| Outcome: | The proposed methods outperform BPE in a low-resource translation setting. |
KazNERD: Kazakh Named Entity Recognition Dataset (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a subtask of information extraction aimed at identifying named entities (NEs) in semi-or unstructured text and classifying them into pre-specified types. |
| Approach: | They present a dataset for Kazakh named entity recognition using an annotation scheme and guidelines for annotation. |
| Outcome: | The dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
Kardeş-NLU: Transfer to Low-Resource Languages with Big Brother’s Help – A Benchmark and Evaluation for Turkic Languages (2024.eacl-long)
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| Challenge: | Cross-lingual transfer (XLT) driven by massively multilingual language models (mmLMs) has been shown to be ineffective for low-resource (LR) target languages with little (or no) representation in mmLM’s pretraining . |
| Approach: | They propose a benchmark to evaluate cross-lingual transfer (XLT) to LR languages that do have a close HR relative and a framework to integrate Turkish into XLT. |
| Outcome: | The proposed configuration is of practical relevance for more of the world’s languages: XLT to LR languages that do have a close HR relative. |
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels (2022.findings-naacl)
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| Challenge: | Existing methods to train pre-trained language models for zero-shot cross-lingual tasks are noisy and lack confidence. |
| Approach: | They propose an uncertainty-aware cross-lingual transfer framework with pseudo-partial-label to maximize the utilization of unlabeled data by reducing noise. |
| Outcome: | The proposed framework outperforms baselines on named entity recognition and natural language inference tasks on 40 languages. |
MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (2024.acl-long)
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| Challenge: | MC2 is the largest open-source corpus of minority languages in china . MC2, however, includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian . |
| Approach: | They propose a multilingual corpus of minority languages in China that includes four underrepresented languages . they prioritize accuracy while enhancing diversity by using a quality-centric approach . |
| Outcome: | The proposed model prioritizes accuracy while enhancing diversity, the authors say . MC2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian . |
Discriminating between Similar Languages on Imbalanced Conversational Texts (L18-1)
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| Challenge: | Empirical results suggest that our system achieves an accuracy of 95.7% on our Uyghur and Kazakh dataset, which is higher than that of the CNN classifier. |
| Approach: | They propose to build a balanced Uyghur and Kazakh corpus and build morphological classifiers to discriminate between the two languages. |
| Outcome: | The proposed system outperforms the champions on both test sets B1 and B2. |
Cross-Lingual Word Embeddings for Turkic Languages (2020.lrec-1)
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| Challenge: | Existing techniques to align monolingual embeddings are difficult to use because of low resources. |
| Approach: | They propose to use existing techniques to align monolingual embedding spaces for Turkic, Uzbek, Azeri, Kazakh and Kyrgyz languages. |
| Outcome: | The proposed techniques outperform existing techniques on bilingual dictionaries and an extrinsic task. |
Qorǵau: Evaluating Safety in Kazakh-Russian Bilingual Contexts (2025.findings-acl)
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Maiya Goloburda, Nurkhan Laiyk, Diana Turmakhan, Yuxia Wang, Mukhammed Togmanov, Jonibek Mansurov, Askhat Sametov, Nurdaulet Mukhituly, Minghan Wang, Daniil Orel, Zain Muhammad Mujahid, Fajri Koto, Timothy Baldwin, Preslav Nakov
| Challenge: | Large language models (LLMs) have the potential to generate harmful content, posing risks to users. |
| Approach: | They propose a dataset specifically designed for safety evaluation in Kazakh and Russian . they use a bilingual context in Kazakhstan where both Kazakh (a low-resource language) and Russian (a high-resourced language) |
| Outcome: | The proposed dataset is designed for safety evaluation in Kazakh and Russian . it shows that both multilingual and language-specific LLMs perform better than others . |
KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics (2022.lrec-1)
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| Challenge: | Text-to-speech (TTS) is a process of converting written text into speech. |
| Approach: | They present an expanded version of their text-to-speech corpus for Kazakh . they propose to use the corpus to build high-quality TTS systems for the language . |
| Outcome: | The constructed corpus is sufficient to build robust TTS models for Kazakh and other Turkic languages, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. |
MiLiC-Eval: Benchmarking Multilingual LLMs for China’s Minority Languages (2025.findings-acl)
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| Challenge: | Large language models excel in high-resource languages but struggle with low-resourced languages . minority languages such as Tibetan, Uyghur, Kazakh, and Mongolian are marginalized in NLP research due to limited digital representation and the scarcity of training data. |
| Approach: | They propose a benchmark for minority languages in China that tracks the progress of large language models on low-resource languages. |
| Outcome: | The proposed benchmark focuses on underrepresented writing systems and syntax-intensive tasks. |
Stereotype Bias in a Bilingual Setting: A Culturally Grounded Evaluation in Kazakhstan (2026.acl-long)
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Nurkhan Laiyk, Daniil Orel, Ayana Mussabayeva, Maiya Goloburda, Kamila Kuishibekova, Liya Goloburda, Diana Turmakhan, Preslav Nakov, Yuxia Wang, Fajri Koto
| Challenge: | Stereotype bias in language models is largely understudied in English . language models perform strongly on downstream NLP tasks, but they are pre-trained on large text corpora . |
| Approach: | They use a dataset to assess stereotype bias in language models in Kazakhstan . they find that stereotype bias is most pronounced in code-mixed inputs . |
| Outcome: | The proposed dataset shows that stereotype bias is most pronounced in code-mixed inputs. |
KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan (2025.acl-long)
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Mukhammed Togmanov, Nurdaulet Mukhituly, Diana Turmakhan, Jonibek Mansurov, Maiya Goloburda, Akhmed Sakip, Zhuohan Xie, Yuxia Wang, Bekassyl Syzdykov, Nurkhan Laiyk, Alham Fikri Aji, Ekaterina Kochmar, Preslav Nakov, Fajri Koto
| Challenge: | Kazakh language remains underrepresented in the field of natural language processing despite the country's population exceeding twenty million . however, there is a lack of dedicated models and benchmark evaluations specifically tailored to Kazakh languages. |
| Approach: | They propose to create a dataset specifically designed for Kazakh language with 23,000 questions sourced from authentic educational materials and manually validated by native speakers and educators. |
| Outcome: | The first MMLU-style dataset specifically designed for Kazakh language. |
KazParC: Kazakh Parallel Corpus for Machine Translation (2024.lrec-main)
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| Challenge: | Statistical machine translation gained ground over rule-based machine translation in the late 1990s thanks to its ability to learn from large bilingual corpora. |
| Approach: | They propose to develop a parallel corpus for machine translation across Kazakh, English, Russian, and Turkish. |
| Outcome: | The proposed model outperforms Google Translate and Yandex Translate in terms of performance and evaluation metrics. |
KazQAD: Kazakh Open-Domain Question Answering Dataset (2024.lrec-main)
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| Challenge: | KazQAD contains just under 6,000 unique questions with extracted short answers and nearly 12,000 passage-level relevance judgements. |
| Approach: | They introduce a Kazakh open-domain question answering dataset that can be used in reading comprehension and full ODQA settings. |
| Outcome: | The proposed dataset can be used in reading comprehension and full ODQA settings, as well as for information retrieval experiments. |
TUMLU: A Unified and Native Language Understanding Benchmark for Turkic Languages (2025.acl-long)
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Jafar Isbarov, Arofat Akhundjanova, Mammad Hajili, Kavsar Huseynova, Dmitry Gaynullin, Anar Rzayev, Osman Tursun, Aizirek Turdubaeva, Ilshat Saetov, Rinat Kharisov, Saule Belginova, Ariana Kenbayeva, Amina Alisheva, Abdullatif Köksal, Samir Rustamov, Duygu Ataman
| Challenge: | preparing native language MMLU benchmarks is costly and limits representativeness of evaluation datasets. |
| Approach: | They propose to use a Turkic language MMLU benchmark to assess massive multitask language understanding capabilities. |
| Outcome: | The proposed benchmarks are based on a Turkic language morphosyntactic and cultural benchmark . the benchmarks evaluate a diverse range of open and proprietary multilingual large language models . |