UQA: A Large-Scale Urdu Question Answering Dataset Translated from SQuAD2.0

核心概念

This paper introduces UQA, a novel dataset for question answering and text comprehension in Urdu, a low-resource language with over 70 million native speakers. UQA is generated by translating the Stanford Question Answering Dataset (SQuAD2.0) using a technique called EATS (Enclose to Anchor, Translate, Seek), which preserves the answer spans in the translated context paragraphs.

要約

The paper presents the process of creating a large-scale question answering dataset for the Urdu language, called UQA, by translating the English SQuAD2.0 dataset. The key highlights are:

Urdu is a low-resource language with over 70 million native speakers, but lacks high-quality NLP datasets. The authors aim to address this gap by creating UQA.

The authors evaluated two machine translation models, Google Translator and Seamless M4T, to select the best one for translating SQuAD2.0 into Urdu. Seamless M4T was found to provide superior translation quality.

To accurately map the answer spans from the English context to the Urdu context, the authors developed the EATS (Enclose to Anchor, Translate, Seek) technique. This involves enclosing the answer in the English text with delimiters, translating the text, and then seeking the answer position in the Urdu text.

The authors benchmarked several state-of-the-art multilingual QA models on UQA, including mBERT, XLM-RoBERTa, and mT5. The XLM-RoBERTa-XL model achieved an F1 score of 85.99 and an Exact Match score of 74.56 on the dataset.

UQA is a valuable resource for developing and testing multilingual NLP systems for Urdu and for enhancing the cross-lingual transferability of existing models. The dataset and code are publicly available.

統計

The UQA dataset contains 124,745 questions in the train set and 11,466 questions in the dev set.
Out of the total 142,177 questions, only 5,966 (4.2%) were discarded due to issues with retaining the quotation marks around the answer.

引用

"UQA is a valuable resource for developing and testing multilingual NLP systems for Urdu and for enhancing the cross-lingual transferability of existing models."
"The UQA dataset and the code are publicly available at www.github.com/sameearif/UQA."

抽出されたキーインサイト

UQA: Corpus for Urdu Question Answering

by Samee Arif,S... 場所 arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01458.pdf

深掘り質問

How can the UQA dataset be further expanded with domain-specific data to create specialized Urdu question answering models for applications like healthcare or education?

To expand the UQA dataset with domain-specific data for applications like healthcare or education, a targeted approach is necessary. Here are some steps that can be taken:

Data Collection: Gather domain-specific texts, articles, research papers, and documents related to healthcare or education in Urdu. This can include medical journals, educational materials, patient information leaflets, etc.

Annotation: Annotate the collected data with question-answer pairs relevant to the domain. This process may involve domain experts to ensure the accuracy and relevance of the questions and answers.

Fine-tuning Models: Utilize the expanded dataset to fine-tune existing question-answering models like XLM-R-XL on domain-specific data. This process helps the model to better understand and respond to queries in the healthcare or education domain.

Evaluation: Evaluate the performance of the fine-tuned models on the domain-specific data to ensure that they provide accurate and relevant answers to questions in the specified domain.

Iterative Process: Continuously update and refine the dataset based on feedback and new information in the healthcare or education domain. This iterative process helps in improving the model's performance over time.

By following these steps, the UQA dataset can be enriched with domain-specific data, enabling the creation of specialized Urdu question-answering models tailored for applications in healthcare or education.

What are the potential challenges in using machine translation to create high-quality datasets for low-resource languages, and how can they be addressed beyond the techniques used in this work?

Using machine translation to create high-quality datasets for low-resource languages poses several challenges, including:

Translation Accuracy: Machine translation may not always accurately capture the nuances and context of the source language, leading to errors in the translated text. This can be addressed by incorporating post-editing by bilingual experts to improve translation quality.

Linguistic Differences: Low-resource languages may have unique linguistic structures, idiomatic expressions, and vocabulary that are challenging for machine translation systems. Customizing machine translation models for specific language pairs can help overcome this challenge.

Data Sparsity: Low-resource languages often lack sufficient parallel corpora for training machine translation models, leading to limited vocabulary coverage and translation quality. Data augmentation techniques and leveraging transfer learning from related languages can help mitigate data sparsity issues.

Domain Specificity: Translating domain-specific terms and concepts accurately can be challenging for machine translation systems. Creating domain-specific translation models or incorporating domain-specific dictionaries can improve translation quality in specialized domains.

Evaluation Metrics: Establishing robust evaluation metrics for assessing the quality of machine-translated datasets in low-resource languages is crucial. Beyond traditional metrics like BLEU score, incorporating human evaluation and domain-specific criteria can provide a more comprehensive assessment.

By addressing these challenges through a combination of human intervention, domain expertise, data augmentation, and specialized model training, the quality of machine-translated datasets for low-resource languages can be significantly enhanced.

Given the differences in language structure between English and Urdu, how can the EATS technique be generalized to create datasets for other language pairs with significant structural differences?

The EATS (Enclose to Anchor, Translate, Seek) technique can be generalized to create datasets for other language pairs with significant structural differences by following these steps:

Identifying Structural Variances: Understand the specific structural differences between the source and target languages, such as word order, grammatical rules, and idiomatic expressions.

Adapting Delimiters: Modify the delimiters used in the EATS technique to accommodate the structural nuances of the target language. This may involve using different markers or symbols to enclose answer spans in the context paragraphs.

Customizing Translation Models: Tailor machine translation models to account for the linguistic variations between the language pairs. This customization can involve training models on parallel corpora that capture the structural differences effectively.

Post-Translation Validation: Implement a validation step post-translation to ensure that the answer spans are accurately preserved in the translated context paragraphs. This may involve manual verification or the use of language-specific tools for alignment.

Iterative Refinement: Continuously refine the EATS process based on feedback and evaluation results to improve the accuracy and quality of the translated datasets for language pairs with significant structural differences.

By adapting the EATS technique to suit the linguistic characteristics of different language pairs, researchers can effectively create high-quality datasets for diverse languages, enabling the development of multilingual question-answering systems across a wide range of linguistic contexts.