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DORE: A Dataset For Portuguese Definition Generation

Core Concepts
Introducing DORE, the first dataset for automatic generation of definitions in Portuguese, facilitating research and study in wider contexts.
Abstract Definition modeling (DM) is crucial for generating dictionary definitions automatically. Portuguese lacks a DM dataset, prompting the creation of DORE with over 100,000 definitions. Evaluation of deep learning based DM models on DORE was conducted. Introduction Definitions are essential for communication and understanding. Manual definition creation is challenging and costly. DM addresses these challenges by automatically generating definitions. Related Work Various datasets have been established for DM in different languages. DM models have evolved from RNN-based to transformer models. Dataset Construction Data collection involved scraping from online dictionaries. DORE dataset includes definitions and contexts for Portuguese words. Methods Deep learning models like transformers and LLMs were used to evaluate DORE. LLMs outperformed other models in the DM task. Results LLMs showed superior performance in generating definitions. Evaluation metrics like BLEU and TER were used to compare model performance. Conclusion DORE was introduced as the first dataset for automatic definition generation in Portuguese. LLMs demonstrated better performance in the DM task. Bibliographical References References to key research papers in the field of definition modeling.
Portuguese is spoken by more than 200 million native speakers. DORE dataset comprises 103,019 definitions. Models like mBART and GPT were used for evaluation.
"As far as we know, this is the first time that LLMs are evaluated on low-resource DM." "LLMs designed for text generation excel in the DM task in Portuguese."

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by Anna... at 03-28-2024

Deeper Inquiries

How can the DORE dataset impact the development of NLP tools for low-resource languages?

The DORE dataset can have a significant impact on the development of NLP tools for low-resource languages by serving as a valuable resource for training machine learning models. Since low-resource languages often lack the necessary annotated datasets required for training NLP models effectively, the availability of a dataset like DORE specifically tailored for Portuguese can bridge this gap. Researchers and developers can leverage DORE to train and fine-tune models for various NLP tasks, such as machine translation, sentiment analysis, and text generation, in the Portuguese language. By providing a substantial amount of annotated data, DORE can enable the development of more accurate and robust NLP tools for low-resource languages, ultimately improving the accessibility and quality of NLP applications for speakers of these languages.

What are the ethical considerations when using publicly available data for research purposes?

When using publicly available data for research purposes, several ethical considerations must be taken into account to ensure responsible and ethical conduct. Some key ethical considerations include: Data Privacy: Researchers must respect the privacy rights of individuals whose data is included in the publicly available datasets. It is essential to anonymize and aggregate data to prevent the identification of individuals. Data Usage: Researchers should adhere to the terms of use and licensing agreements associated with the publicly available datasets. They should ensure that the data is used only for the intended research purposes and not for any unauthorized or unethical activities. Informed Consent: If the publicly available data contains personal or sensitive information, researchers must ensure that proper informed consent was obtained for data collection and sharing. Transparency: Researchers should be transparent about the sources of the data, the methods used for data collection and processing, and any potential biases or limitations in the dataset. Data Security: Researchers must take measures to secure the data and prevent unauthorized access or misuse of the information contained in the dataset.

How can the findings of this research contribute to the broader field of natural language processing?

The findings of this research can make several significant contributions to the broader field of natural language processing: Dataset Creation: The creation of the DORE dataset fills a crucial gap by providing the first dataset for Portuguese Definition Generation. This dataset can serve as a benchmark for future research in Portuguese NLP tasks. Model Evaluation: By evaluating several deep learning-based Definition Modelling models on the DORE dataset, the research provides insights into the performance of these models in the context of Portuguese language processing. Transfer Learning: The evaluation of popular Large Language Models (LLMs) on the DORE dataset, especially in a zero-shot approach, can offer valuable insights into the effectiveness of transfer learning in low-resource language tasks. Open Access: The release of the DORE dataset and corresponding models under a Creative Commons license promotes open science and encourages collaboration and further research in the field of NLP for Portuguese and other low-resource languages.