MLQA - Multilingual Question-Answering
Multilingual Question-Answering Dataset
@kaggle.thedevastator_mlqa_multilingual_question_answering_dataset
Multilingual Question-Answering Dataset
@kaggle.thedevastator_mlqa_multilingual_question_answering_dataset
By mlqa (From Huggingface) [source]
The dataset consists of several files in CSV format that provide context passages or paragraphs along with corresponding questions and answer options. The context passages serve as the source of information from which the questions are derived, and the answer options are potential answers to these questions.
Each file in the dataset contains different language combinations for evaluation purposes. For example, mlqa.es.zh_test.csv focuses on testing multilingual question-answering models in Spanish and Chinese languages. Similarly, mlqa.hi.de_test.csv provides test data specifically for evaluating Hindi-German language pairs.
In order to facilitate accurate evaluation of models' performance, each file includes multiple columns for context and answers. This allows researchers to assess how well their models can generate correct answers based on the given contexts.
- Evaluation of multilingual question-answering models: This dataset can be used to evaluate the performance of different models designed for multilingual question-answering. By providing context, question, and answer pairs in multiple languages, it allows researchers to measure the accuracy and effectiveness of their models across different language pairs.
- Cross-lingual transfer learning: The MLQA dataset can be utilized to develop cross-lingual transfer learning techniques. Models trained on this dataset can learn to perform question-answering tasks in one language and then transfer that knowledge to answer questions in another language.
- Language understanding research: Researchers studying natural language processing (NLP) and language understanding can use this dataset to analyze how different languages handle questions and answers within various contexts. They can explore linguistic patterns, variations, and differences across languages by comparing the performance of NLP models trained on this dataset for both similar and dissimilar language pairs
If you use this dataset in your research, please credit the original authors.
Data Source
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.
File: mlqa.es.zh_test.csv
| Column name | Description |
|---|---|
| context | The text passage or paragraph in which a question is being asked. (Text) |
| answers | The possible answers to the question, along with their start and end positions within the context passage. (Text) |
File: mlqa.hi.de_test.csv
| Column name | Description |
|---|---|
| context | The text passage or paragraph in which a question is being asked. (Text) |
| answers | The possible answers to the question, along with their start and end positions within the context passage. (Text) |
File: mlqa.zh.de_test.csv
| Column name | Description |
|---|---|
| context | The text passage or paragraph in which a question is being asked. (Text) |
| answers | The possible answers to the question, along with their start and end positions within the context passage. (Text) |
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit mlqa (From Huggingface).
CREATE TABLE mlqa_ar_ar_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_ar_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_de_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_de_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_en_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_en_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_es_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_es_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_hi_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_hi_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_vi_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_vi_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_zh_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_ar_zh_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_ar_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_ar_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_de_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_de_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_en_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_en_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_es_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_es_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_hi_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_hi_validation (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
);CREATE TABLE mlqa_de_vi_test (
"context" VARCHAR,
"question" VARCHAR,
"answers" VARCHAR,
"id" VARCHAR
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