Papers with low-resource language
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline (2021.eacl-main)
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Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, Huseyin Atakan Varol
| Challenge: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
| Approach: | They propose to build an open-source Kazakh speech corpus for the Kazakh language that contains over 153,000 transcribed audio . they describe the data collection and preprocessing procedures followed by a description of the database specifications. |
| Outcome: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
| Approach: | They exploit linguistic overlap to facilitate translation to and from low-resource languages . they use monolingual data and parallel data in related high-resourced languages based on their method . |
| Outcome: | The proposed method significantly improves translation into low-resource language compared to baselines on 7 languages from three different language families. |
Progressive Sentiment Analysis for Code-Switched Text Data (2022.findings-emnlp)
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| Challenge: | Multilingual transformer language models are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition. |
| Approach: | They propose a framework that takes the distinction between resource-rich and low-resource language into account and progressively trains from resource-dominated to low-rsource samples. |
| Outcome: | The proposed model outperforms existing methods on low-resource languages and performs well on medium-resourced to high-res languages. |
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)
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| Challenge: | Several strategies have been proposed to enhance performance in low-resource scenarios. |
| Approach: | They propose to use 5 low-resource strategies for dependency parsing for multiple languages . they use ensembled approach on 7 UD low-rsource languages based on their results . |
| Outcome: | The proposed approach improves on a low-resource language Sanskrit. |
Cross-Lingual Machine Reading Comprehension (D19-1)
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020.findings-emnlp)
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| Challenge: | Existing suicide dictionaries for other languages have been limited to Korean . a model that uses social media data to identify whether a post includes suicidal ideation is useful . |
| Approach: | They propose a model that uses existing suicide dictionaries for Korean to predict suicidal ideation . they use the existing dictionary for English and Chinese to translate a post into English and then use the separate suicide-oriented embeddings for English. |
| Outcome: | The proposed model can detect whether a given social media post includes suicidal ideation in Korean . it uses existing suicide dictionaries for other languages to translate the post into English and Chinese, and then embeds the suicide-oriented embeddings for English and China. |
A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)
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| Challenge: | despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents. |
| Approach: | They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles . |
| Outcome: | The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. |
Constructing Indonesian-English Travelogue Dataset (2024.lrec-main)
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| Challenge: | low-resource language research often hampered due to under-representation of how it is being used in reality. |
| Approach: | They propose to use a dataset comprising both Indonesian and English from personal travelogue articles . they used named and nominal expressions of four entity types related to travel . |
| Outcome: | The proposed dataset is more representative of how Indonesian language is being used in reality. |
Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies (2020.lrec-1)
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| Challenge: | Icelandic language is low-resource and is not yet considered in imminent danger . efforts underway to make it accessible and usable in Language Technology . |
| Approach: | They propose to build a parallel Icelandic dependency treebank based on Universal Dependencies (UD) this is the first parallel treebank resource for the language and several other languages already have one . |
| Outcome: | The proposed treebank is the first parallel treebank resource for the low-resource language . the project will be published as part of UD version 2.6. |
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)
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| Challenge: | Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation. |
| Approach: | They propose a method for generating and validating QA datasets for low-resource languages . they use English data as context to generate synthetic multiple-choice (MC) question-answer pairs . |
| Outcome: | The proposed method maintains quality, reduces likelihood of factual errors, and circumvents costly annotation. |
Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
| Approach: | They evaluated five languages using a parallel corpus of 1,000 instances each and found a zero-shot improvement of 7.4 from the baseline score of 7.1 to a score of 15.5 at best. |
| Outcome: | The proposed model improves performance in the linguistically diverse country of Indonesia by 7.4 from baseline score of 7.1 to 15.5 at best. |
Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation (2025.coling-main)
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| Challenge: | Large language models (LLMs) are mainly trained on English data and struggle with low-resource languages. |
| Approach: | They propose to add a new language to Llama to improve classification accuracy for Persian tasks by aligning representations through bilingual pretraining and instruction datasets. |
| Outcome: | The proposed model performs on generation and classification tasks with no adverse impact and sometimes even improvements on English tasks. |
Exploring the Potential of Large Language Models (LLMs) for Low-resource Languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali Language (2024.lrec-main)
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| Challenge: | Large Language Models excel in various tasks like Named Entity Recognition and Part-of-Speech tagging. |
| Approach: | They propose to use large language models to perform NLP tasks such as Named Entity Recognition and Part-of-Speech tagging in Nepali. |
| Outcome: | The proposed models perform better than other approaches for Nepali NER and POS tagging tasks. |
Macedon: Minimizing Representation Coding Rate Reduction for Cross-Lingual Natural Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing approaches to learn cross-lingual models require limited data to perform cross-linguistic tasks. |
| Approach: | They propose a method to remove language-associated information via minimizing representation coding rate reduction. |
| Outcome: | The proposed model outperforms state-of-the-art models on cross-lingual tasks. |
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)
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Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi
| Challenge: | Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations. |
| Approach: | They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. |
| Outcome: | The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics. |