Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages

• URL: http://arxiv.org/abs/2605.02608v1
• Date: Mon, 04 May 2026 13:55:32 GMT
• Title: Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
• Abstract summary: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages.<n>We evaluate four transformer models across ten typologically diverse languages.<n>We find that the Biaffine LSTM consistently outperforms transformer models in low-resource regimes.
• Score: 2.1297158600098287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood. We evaluate four parsers -- the Biaffine LSTM, Stack-Pointer Network, AfroXLMR-large, and RemBERT -- across ten typologically diverse languages, with a focus on low-resource African languages. We find that the Biaffine LSTM consistently outperforms transformer models in low-resource regimes, with transformers recovering their advantage as training data increases. The crossover falls within a resource range typical of treebanks for under-resourced languages. Morphological complexity (measured via MATTR) emerges as a significant secondary predictor of transformers' relative disadvantage after controlling for corpus size. These results indicate that the Biaffine LSTM may be better suited for syntactic tool development in low-resource regimes until sufficient annotated data is available to leverage the representational capacity of pre-trained transformers.

Related papers

• Transport and Merge: Cross-Architecture Merging for Large Language Models [59.53629883788284]
  Large language models (LLMs) achieve strong capabilities by scaling model capacity and training data.<n>Many real-world deployments rely on smaller models trained or adapted from low-resource data.<n>This gap motivates the need for mechanisms to transfer knowledge from large, high-resource models to smaller, low-resource targets.
  arXiv  Detail & Related papers  (2026-02-05T09:57:57Z)
• Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation [49.82863380286994]
  In-context learning may offer novel ways to adapt Large Language Models for low-resource machine translation.<n>In this study, we explore scaling low-resource machine translation ICL beyond the few-shot setting to thousands of examples with long-context models.<n>Our experiments on Javanese and Sundanese show that gains from additional context saturate quickly and can degrade near the maximum context window.
  arXiv  Detail & Related papers  (2026-02-04T17:02:22Z)
• Transformer-Based Low-Resource Language Translation: A Study on Standard Bengali to Sylheti [0.0]
  We investigate Bengali-to-Sylheti translation by fine-tuning multilingual Transformer models.<n> Experimental results demonstrate that fine-tuned models significantly outperform large language models.
  arXiv  Detail & Related papers  (2025-10-20T16:29:24Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation [62.202893186343935]
  We explore what it would take to adapt Large Language Models for low-resource languages. We show that parallel data is critical during both pre-training andSupervised Fine-Tuning (SFT) Our experiments with three LLMs across two low-resourced language groups reveal consistent trends, underscoring the generalizability of our findings.
  arXiv  Detail & Related papers  (2024-08-23T00:59:38Z)
• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation [7.151108031568037]
  This paper constructs low-resource relation extraction datasets in 10 low-resource languages (LRLs) in three regions (Central Asia, Southeast Asia and Middle East) The corpora are constructed by translating the original publicly available English RE datasets (NYT10, FewRel and CrossRE) using an effective multilingual machine translation. Then, we use the language perplexity (PPL) to filter out the low-quality data from the translated datasets.
  arXiv  Detail & Related papers  (2024-06-17T03:02:04Z)
• Enhancing Neural Machine Translation of Low-Resource Languages: Corpus Development, Human Evaluation and Explainable AI Architectures [0.0]
  The Transformer architecture stands out as the gold standard, especially for high-resource language pairs. The scarcity of parallel datasets for low-resource languages can hinder machine translation development. This thesis introduces adaptNMT and adaptMLLM, two open-source applications streamlined for the development, fine-tuning, and deployment of neural machine translation models.
  arXiv  Detail & Related papers  (2024-03-03T18:08:30Z)
• Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction [18.926993352330797]
  We propose TransFusion, a framework in which models are fine-tuned to use English translations of low-resource language data. GoLLIE-TF, a cross-lingual instruction-tuned LLM for IE tasks, is designed to close the performance gap between high and low-resource languages.
  arXiv  Detail & Related papers  (2023-05-23T01:23:22Z)
• Bayesian Transformer Language Models for Speech Recognition [59.235405107295655]
  State-of-the-art neural language models (LMs) represented by Transformers are highly complex. This paper proposes a full Bayesian learning framework for Transformer LM estimation.
  arXiv  Detail & Related papers  (2021-02-09T10:55:27Z)
• Optimizing Transformer for Low-Resource Neural Machine Translation [4.802292434636455]
  Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. Our experiments on different subsets of the IWSLT14 training data show that the effectiveness of Transformer under low-resource conditions is highly dependent on the hyper- parameter settings. Using an optimized Transformer for low-resource conditions improves the translation quality up to 7.3 BLEU points compared to using the Transformer default settings.
  arXiv  Detail & Related papers  (2020-11-04T13:12:29Z)

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.