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Multilingual Question Answering Dataset

8 Language Variations with Complex Question Types

By Huggingface Hub [source]

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About this dataset

The AmazonScience/mintaka dataset is an invaluable resource for training advanced end-to-end models in multilingual, complex natural language question answering. With 8 additional languages—Arabic, French, German, Hindi, Italian, Japanese, Portuguese and Spanish—in addition to the English original it is ideal to develop robust and comprehensive models that are able to tackle a broad range of questions. The dataset has been carefully partitioned into train and test splits for each language so that models can be built at various complexity levels while enjoying accurate evaluation results. Data points contain important distinguishing features such as questions category, complexity type and entities related both to the question and the answer itself making training even more efficient. The combination of all these elements make the AmazonScience/mintaka dataset a powerful tool for building state-of-the-art natural language processing models that can tackle complex conversational challenges with true multilingual accuracy

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How to use the dataset

This guide is designed to demonstrate how to use the AmazonScience/mintaka dataset for creating multilingual, complex natural language question-answering models.

Step 1: Set up the environment

To start working with the AmazonScience/mintaka dataset you'll need to set up a workspace that provides access to adequate computational resources. This can be determined based on your specific hardware and software requirements for training ML models. Additionally, you should install commonly used ML packages into this environment such as TensorFlow and PyTorch. Once you've completed these steps, you are now ready to begin exploring the dataset and its associated data fields.

Step 2: Explore Attribute Values in the Dataset

The AmazonScience/mintaka dataset contains questions and answers in 8 languages for model training and evaluation. The attributes included in this dataset are lang (language of the question), answerText (the answer to each question), category (the category of each question), complexityType (the complexity type of each question) , “questionEntity”( a brief description or definition about related entities in questions) ,’answerEntity’(a brief description or definition about related entities in answers); as well as three subsets—train.csv, dev.csv, test.csv—corresponding respectively with training set, development set and test sets; lang codes provide aid in identifying languages between datasets (ej., eng = English).

Columns:lang** ; answerText** ; category **; complexityType** ;questionEntity **; answerEntity **; lang** ; train_csv ; dev_csv ;test_csv  

           *asterisks indicate fields required for model building                  

Step 3: Prepare Data For Model Building

    Before attempting complex questions-answers tasks via machine learning algorithms it's important that data within your ML model undergoes approval processes—namely combination or elimination of duplicate values whether they're fillable entities or literal strings within a text string matching similar measures found by different names accommodating multiple languages throughout your ML model builder algorithm's task execution aside from manual entry validations by editors hopping to keep consistent assigned labels per language .There is flexibility according previous research on which methods requested particular metrics chosen fot his project often changing task examples meant make sure chunks remain clear plus make sure covered concerns since increase project risk

Research Ideas

• Developing machine learning pipelines that provide deep understanding of context and accuracy in answering natural language questions, taking into account these 8 different languages.
• Designing a complex question-answering application which can be deployed on different language platforms to provide multi-lingual support for natural language queries.
• Enhancing Natural Language Processing techniques by introducing multilingual complexity to the system so it can further refine the handling of questions and answers in various languages

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

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File: train.csv

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File: test.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.