An Analysis of Language Frequency and Error Correction for Esperanto

• URL: http://arxiv.org/abs/2402.09696v2
• Date: Fri, 16 Feb 2024 02:19:49 GMT
• Title: An Analysis of Language Frequency and Error Correction for Esperanto
• Authors: Junhong Liang
• Abstract summary: We conduct a comprehensive frequency analysis using the Eo-GP dataset. We then introduce the Eo-GEC dataset, derived from authentic user cases. Using GPT-3.5 and GPT-4, our experiments show that GPT-4 outperforms GPT-3.5 in both automated and human evaluations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Grammar Error Correction (GEC) initiatives tend to focus on major languages, with less attention given to low-resource languages like Esperanto. In this article, we begin to bridge this gap by first conducting a comprehensive frequency analysis using the Eo-GP dataset, created explicitly for this purpose. We then introduce the Eo-GEC dataset, derived from authentic user cases and annotated with fine-grained linguistic details for error identification. Leveraging GPT-3.5 and GPT-4, our experiments show that GPT-4 outperforms GPT-3.5 in both automated and human evaluations, highlighting its efficacy in addressing Esperanto's grammatical peculiarities and illustrating the potential of advanced language models to enhance GEC strategies for less commonly studied languages.

Related papers

• GPT-3.5 for Grammatical Error Correction [0.4757470449749875]
  This paper investigates the application of GPT-3.5 for Grammatical Error Correction (GEC) in multiple languages. We conduct automatic evaluations of the corrections proposed by GPT-3.5 using several methods. For English, GPT-3.5 demonstrates high recall, generates fluent corrections, and generally preserves sentence semantics. But, human evaluation for both English and Russian reveals that, despite its strong error-detection capabilities, GPT-3.5 struggles with several error types.
  arXiv  Detail & Related papers  (2024-05-14T09:51:09Z)
• Wav2Gloss: Generating Interlinear Glossed Text from Speech [78.64412090339044]
  We propose Wav2Gloss, a task in which four linguistic annotation components are extracted automatically from speech. We provide various baselines to lay the groundwork for future research on Interlinear Glossed Text generation from speech.
  arXiv  Detail & Related papers  (2024-03-19T21:45:29Z)
• Native Language Identification with Large Language Models [60.80452362519818]
  We show that GPT models are proficient at NLI classification, with GPT-4 setting a new performance record of 91.7% on the benchmark11 test set in a zero-shot setting. We also show that unlike previous fully-supervised settings, LLMs can perform NLI without being limited to a set of known classes.
  arXiv  Detail & Related papers  (2023-12-13T00:52:15Z)
• SOUL: Towards Sentiment and Opinion Understanding of Language [96.74878032417054]
  We propose a new task called Sentiment and Opinion Understanding of Language (SOUL) SOUL aims to evaluate sentiment understanding through two subtasks: Review (RC) and Justification Generation (JG)
  arXiv  Detail & Related papers  (2023-10-27T06:48:48Z)
• FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation [11.421545095092815]
  FlaCGEC is a new CGEC dataset featured with fine-grained linguistic annotation. We collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually. We evaluate various cutting-edge CGEC methods on the proposed FlaCGEC dataset and their unremarkable results indicate that this dataset is challenging in covering a large range of grammatical errors.
  arXiv  Detail & Related papers  (2023-09-26T10:22:43Z)
• A negation detection assessment of GPTs: analysis with the xNot360 dataset [9.165119034384027]
  Negation is a fundamental aspect of natural language, playing a critical role in communication and comprehension. We focus on the identification of negation in natural language using a zero-shot prediction approach applied to our custom xNot360 dataset. Our findings expose a considerable performance disparity among the GPT models, with GPT-4 surpassing its counterparts and GPT-3.5 displaying a marked performance reduction.
  arXiv  Detail & Related papers  (2023-06-29T02:27:48Z)
• Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning [96.13057811149827]
  We propose Inference-time Policy Adapters (IPA), which efficiently tailors a language model without fine-tuning it. IPA guides a large base model during decoding time through a lightweight policy adapter trained to optimize an arbitrary user objective. It consistently brings significant improvements over off-the-shelf language models.
  arXiv  Detail & Related papers  (2023-05-24T11:52:55Z)
• Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation [41.94480044074273]
  ChatGPT is a large-scale language model based on the advanced GPT-3.5 architecture. We design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT. Our evaluation involves assessing ChatGPT's performance on five official test sets in three different languages, along with three document-level GEC test sets in English.
  arXiv  Detail & Related papers  (2023-04-04T12:33:40Z)
• Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction [28.58384091374763]
  GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks. We perform experiments testing the capabilities of a GPT-3.5 model (text-davinci-003) and a GPT-4 model (gpt-4-0314) on major GEC benchmarks. We report the performance of our best prompt on the BEA-2019 and JFLEG datasets, finding that the GPT models can perform well in a sentence-level revision setting.
  arXiv  Detail & Related papers  (2023-03-25T03:08:49Z)
• CLSE: Corpus of Linguistically Significant Entities [58.29901964387952]
  We release a Corpus of Linguistically Significant Entities (CLSE) annotated by experts. CLSE covers 74 different semantic types to support various applications from airline ticketing to video games. We create a linguistically representative NLG evaluation benchmark in three languages: French, Marathi, and Russian.
  arXiv  Detail & Related papers  (2022-11-04T12:56:12Z)
• A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model [100.67378875773495]
  We propose a generic and language-independent strategy for multilingual Grammatical Error Correction. Our approach creates diverse parallel GEC data without any language-specific operations. It achieves the state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian)
  arXiv  Detail & Related papers  (2022-01-26T02:10:32Z)

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.