Vietnamese Question Answering System Research Articles

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents.

Building a Vietnamese question answering system based on knowledge graph and distributed CNN

Building a Vietnamese question answering system based on knowledge graph and distributed CNN

Question Analysis towards a Vietnamese Question Answering System in the Education Domain

Abstract Building a computer system, which can automatically answer questions in the human language, speech or text, is a long-standing goal of the Artificial Intelligence (AI) field. Question analysis, the task of extracting important information from the input question, is the first and crucial step towards a question answering system. In this paper, we focus on the task of Vietnamese question analysis in the education domain. Our goal is to extract important information expressed by named entities in an input question, such as university names, campus names, major names, and teacher names. We present several extraction models that utilize the advantages of both traditional statistical methods with handcrafted features and more recent advanced deep neural networks with automatically learned features. Our best model achieves 88.11% in the F1 score on a corpus consisting of 3,600 Vietnamese questions collected from the fan page of the International School, Vietnam National University, Hanoi.

BERT+vnKG: Using Deep Learning and Knowledge Graph to Improve Vietnamese Question Answering System

A question answering (QA) system based on natural language processing and deep learning is a prominent area and is being researched widely. The Long Short-Term Memory (LSTM) model that is a variety of Recurrent Neural Network (RNN) used to be popular in machine translation, and question answering system. However, that model still has certainly limited capabilities, so a new model named Bidirectional Encoder Representation from Transformer (BERT) emerged to solve these restrictions. BERT has more advanced features than LSTM and shows state-of-the-art results in many tasks, especially in multilingual question answering system over the past few years. Nevertheless, we tried applying multilingual BERT model for a Vietnamese QA system and found that BERT model still has certainly limitation in term of time and precision to return a Vietnamese answer. The purpose of this study is to propose a method that solved above restriction of multilingual BERT and applied for question answering system about tourism in Vietnam. Our method combined BERT and knowledge graph to enhance accurately and find quickly for an answer. We experimented our crafted QA data about Vietnam tourism on three models such as LSTM, BERT fine-tuned multilingual for QA (BERT for QA), and BERT+vnKG. As a result, our model outperformed two previous models in terms of accuracy and time. This research can also be applied to other fields such as finance, e-commerce, and so on.

Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System

Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System