Building a Rich Dataset to Empower the Persian Question Answering Systems
Question answering systems provide short, precise, and specific answers to questions. So far, many robust question answering systems have been developed for English, while some languages with fewer resources, like Persian, have few numbers of standard dataset. In this study, a comprehensive open-domain dataset is presented for Persian. This dataset is called NextQuAD and has 7,515 contexts, including 23,918 questions and answers. Then, a BERT-based question answering model has been applied to this dataset using two pre-trained language models, including ParsBERT and XLM-RoBERTa. The results of these two models have been ensembled using mean logits. Evaluation on the development set shows 0.95 Exact Match (EM) and 0.97 Fl_score. Also, to compare the NextQuAD with other Persian datasets, our trained model on the NextQuAD, is evaluated on two other datasets named PersianQA and ParSQuAD. Comparisons show that the proposed model increased EM by 0.39 and 0.14 respectively in PersianQA and ParSQuAD-manual, while a slight EM decline of 0.007 happened in ParSQuAD-automatic.
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Knowledge-based Approach for Answering Complex Questions in Persian
Research on open-domain question answering (QA) has a long tradition. A challenge in this domain is answering complex questions (CQA) that require complex inference methods and large amounts of knowledge. In low resource…
Open-Domain Question AnsweringQuestion AnsweringPeCoQ: A Dataset for Persian Complex Question Answering over Knowledge Graph
Question answering systems may find the answers to users' questions from either unstructured texts or structured data such as knowledge graphs. Answering questions using supervised learning approaches including deep lear…
Knowledge GraphsQuestion AnsweringPerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language
Medical consumer question answering (CQA) is crucial for empowering patients by providing personalized and reliable health information. Despite recent advances in large language models (LLMs) for medical QA, consumer-ori…
BenchmarkingQuestion AnsweringParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian
Despite recent advances in vision-and-language tasks, most progress is still focused on resource-rich languages such as English. Furthermore, widespread vision-and-language datasets directly adopt images representative o…
Image CaptioningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)PersianLLaMA: Towards Building First Persian Large Language Model
Despite the widespread use of the Persian language by millions globally, limited efforts have been made in natural language processing for this language. The use of large language models as effective tools in various nat…
Language ModelingLanguage ModellingLarge Language ModelMachine Translation+5