Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?

• URL: http://arxiv.org/abs/2505.16814v2
• Date: Mon, 26 May 2025 18:32:38 GMT
• Title: Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?
• Authors: Gaurav Kamath, Sowmya Vajjala,
• Abstract summary: Named Entity Recognition for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available.<n>Data augmentation for increasing the amount of low-resource labeled data is a common practice.<n>Our results suggest that synthetic data does in fact hold promise for low-resource language NER, though we see significant variation between languages.
• Score: 2.7624021966289605
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data augmentation for increasing the amount of low-resource labeled data is a common practice. In this paper, we explore the role of synthetic data in the context of multilingual, low-resource NER, considering 11 languages from diverse language families. Our results suggest that synthetic data does in fact hold promise for low-resource language NER, though we see significant variation between languages.

Related papers

• Natural language processing for African languages [7.884789325654572]
  dissertation focuses on languages spoken in Sub-Saharan Africa where all the indigenous languages can be regarded as low-resourced.<n>We show that the quality of semantic representations learned in word embeddings does not only depend on the amount of data but on the quality of pre-training data.<n>We develop large scale human-annotated labelled datasets for 21 African languages in two impactful NLP tasks.
  arXiv  Detail & Related papers  (2025-06-30T22:26:36Z)
• SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods [1.2091341579150698]
  We release datasets of sentences containing polysemous words across ten low-resource languages.<n>To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method.<n>Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation.
  arXiv  Detail & Related papers  (2025-05-29T17:48:08Z)
• MAGE: Multi-Head Attention Guided Embeddings for Low Resource Sentiment Classification [0.19381162067627603]
  We introduce an advanced model combining Language-Independent Data Augmentation (LiDA) with Multi-Head Attention based weighted embeddings.<n>This approach not only addresses the data scarcity issue but also sets a foundation for future research in low-resource language processing and classification tasks.
  arXiv  Detail & Related papers  (2025-02-25T08:53:27Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages [8.612181075294327]
  We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER.<n>We present a novel formalized projection approach of matching source entities with extracted target candidates.<n>These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for crosslingual named entity recognition in lowresource languages.
  arXiv  Detail & Related papers  (2025-01-30T21:00:47Z)
• Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields [68.17213992395041]
  Low-resource named entity recognition is still an open problem in NLP. We present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly.
  arXiv  Detail & Related papers  (2024-04-14T23:44:49Z)
• Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon [78.12363425794214]
  We focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets.
  arXiv  Detail & Related papers  (2024-02-03T10:41:05Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese [54.00582760714034]
  Cross-lingual NLP transfer can be improved by exploiting data and models of high-resource languages. We release a new web corpus of Faroese and Faroese datasets for named entity recognition (NER), semantic text similarity (STS) and new language models trained on all Scandinavian languages.
  arXiv  Detail & Related papers  (2023-04-18T08:42:38Z)
• Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models [4.168157981135698]
  We show how knowledge can be distilled from Large Language Models (LLMs) to improve upon learned metrics without requiring human annotators. We show that the performance of a BLEURT-like model on lower resource languages can be improved in this way.
  arXiv  Detail & Related papers  (2023-02-07T14:35:35Z)
• Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources [38.814057529254846]
  We examine the characteristics of 156 publicly available NLP datasets. We survey language-proficient NLP researchers and crowd workers per language. We identify strategies for collecting high-quality multilingual data on the Mechanical Turk platform.
  arXiv  Detail & Related papers  (2022-11-28T18:54:33Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.