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Automatically Translating and Aligning SQuAD2.0 to Create a Large-Scale Basque Question Answering Dataset


핵심 개념
This work presents EuSQuAD, the first large-scale, synthetic extractive question answering dataset for the Basque language, created by automatically translating and aligning the SQuAD2.0 dataset.
요약
The authors present EuSQuAD, a new Basque question answering (QA) dataset created by automatically translating and aligning the SQuAD2.0 dataset. The key steps are: Sentence splitting: The English SQuAD2.0 contexts are split into individual sentences. Machine translation: The sentences, questions, and answers are automatically translated from English to Basque using a neural machine translation system. Answer alignment: Due to translation issues, the translated answers often do not match the translated context. The authors use a semantic text similarity approach based on neural language models to align the translated answers to the correct spans in the translated contexts. The resulting EuSQuAD dataset contains over 142k QA examples, making it the largest Basque QA dataset to date. The authors conduct a qualitative analysis and show that the CANINE-s character-based language model performs better than the BERTeus subword-based model for the answer alignment task. To evaluate EuSQuAD, the authors also create a new manually annotated Basque QA test set of 490 questions. Experiments show that models trained on EuSQuAD outperform those trained on the original English SQuAD2.0 dataset, demonstrating the value of EuSQuAD as a training resource for Basque QA systems.
통계
The EuSQuAD dataset contains over 142k QA examples, making it the largest Basque QA dataset to date. The manually annotated Basque QA test set contains 490 questions. The average context length is 727 characters for the training set and 814 characters for the test set. The average question length is 84 characters for the training set and 44 characters for the test set. The average answer length is 22 characters for the training set and 23 characters for the test set.
인용문
"EuSQuAD is the first large-scale, synthetic extractive QA dataset for Basque." "We demonstrate EuSQuAD's value through extensive qualitative analysis and QA experiments supported with EuSQuAD as training data." "Interestingly, the results obtained suggest that embeddings produced by character-based language models are better suited for alignment purposes —within the parameters of our investigation— than the more widespread token-based ones."

에서 추출된 주요 통찰력

by Aito... 위치 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12177.pdf
EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque

심층적인 질문

How could the automatic translation and alignment process be further improved to reduce errors and produce an even higher-quality Basque QA dataset?

To enhance the automatic translation and alignment process for generating a higher-quality Basque QA dataset, several improvements can be considered: Context-Aware Translation: Implement a context-aware translation approach where the answer is translated within the context of the passage. This can help maintain the coherence and relevance of the answer within the context, reducing errors caused by translating answers independently. Fine-Tuning MT Models: Fine-tune machine translation models specifically for the task of translating QA datasets. By training the MT models on a dataset that includes question-answer pairs, the models can learn to preserve the semantic meaning and context during translation. Post-Editing and Correction: Implement a post-editing step where human annotators review and correct the translated answers to ensure accuracy and alignment with the context. This manual review can help catch errors that automated processes might miss. Utilize Bilingual Lexicons: Incorporate bilingual lexicons or dictionaries specific to Basque and English to improve the accuracy of translation. These resources can help in maintaining the semantic equivalence of terms between the two languages. Iterative Alignment Process: Implement an iterative alignment process where the system learns from previous alignment errors and adjusts its approach to improve accuracy over time. This adaptive learning mechanism can help in continuously refining the alignment process.

How could the EuSQuAD dataset be leveraged to advance other Basque NLP tasks beyond question answering, such as machine translation or language modeling?

EuSQuAD can be leveraged to advance various Basque NLP tasks beyond question answering by serving as a valuable resource for training and evaluation. Here are some ways in which EuSQuAD can be utilized for other tasks: Machine Translation: EuSQuAD can be used to train machine translation models specifically for translating between Basque and English. By incorporating QA data into the training process, the models can learn to generate more contextually relevant translations. Language Modeling: EuSQuAD can be used to fine-tune language models for Basque, enabling them to better understand the nuances of the language and improve their performance on a wide range of NLP tasks. The QA data can provide additional context for language modeling tasks. Named Entity Recognition (NER): The passages in EuSQuAD contain named entities that can be used for training NER models in Basque. By extracting and annotating named entities from the dataset, NER models can be trained to identify and classify entities in Basque text. Text Summarization: EuSQuAD can be used to train text summarization models that can generate concise summaries of Basque passages. By leveraging the QA pairs in the dataset, the models can learn to extract key information and generate informative summaries. Sentiment Analysis: The questions and answers in EuSQuAD can be used to train sentiment analysis models for Basque text. By analyzing the sentiment expressed in the QA pairs, models can learn to classify text based on sentiment polarity. By utilizing EuSQuAD for a diverse set of NLP tasks, researchers and developers can advance the capabilities of Basque language processing across various domains.
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