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

Querying knowledge graphs in natural language.

Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks-the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available

A Convolutional Neural Network Based Approach For Visual Question Answering

Computer Vision is a scientific discipline which involves the development of an algorithmic basis for the construction of intelligent systems that aim at analysis, understanding and extraction of useful information from visual data. This visual data can be plain images, video sequences, views from multiple cameras, etc. Natural Language Processing (NLP), is the ability of machines to read and understand human languages. Visual Question Answering (VQA), is a multi-discipline Artificial Intelligence (AI) research problem, which is a combination of Natural Language Processing (NLP), Computer Vision (CV), and Knowledge Reasoning (KR). Given an image and a question related to the image in natural language, the algorithm has to output an accurate natural language answer. Since the questions are open-ended, the system requires a very detailed understanding of the image, its context and a broad set of AI capabilities – object detection, activity recognition and knowledge-based reasoning. Since the release of the VQA dataset in 2014, numerous datasets and algorithms for VQA have been put forward. In this work, we propose a new baseline for the problem of visual question answering. Our model uses a deep residual network (ResNet) to compute the image features and ByteNet to compute question embeddings. A soft attention mechanism is used to focus on most relevant image features and a classifier is used to generate probabilities over an answer set. We implemented the solution in TensorFlow, which is an open source deep-learning platform, developed by Google. iv Prior to using deep residual network (ResNet) and ByteNet, we tried using VGG16 for extracting image features and long short-term memory units (LSTM) for extracting question features. We observed that using ResNet and ByteNet resulted in an improved accuracy when compared to using VGG16 and LSTM. We evaluate our model on three major image question answering datasets: DAQUAR-ALL, COCO-QA and The VQA Dataset. Our model, despite having a relatively simple architecture, achieves 64.6% accuracy on VQA 1.0 dataset and 59.7% accuracy on VQA 2.0 dataset

Evaluating visually grounded language capabilities using microworlds

Deep learning has had a transformative impact on computer vision and natural language processing. As a result, recent years have seen the introduction of more ambitious holistic understanding tasks, comprising a broad set of reasoning abilities. Datasets in this context typically act not just as application-focused benchmark, but also as basis to examine higher-level model capabilities. This thesis argues that emerging issues related to dataset quality, experimental practice and learned model behaviour are symptoms of the inappropriate use of benchmark datasets for capability-focused assessment. To address this deficiency, a new evaluation methodology is proposed here, which specifically targets in-depth investigation of model performance based on configurable data simulators. This focus on analysing system behaviour is complementary to the use of monolithic datasets as application-focused comparative benchmarks. Visual question answering is an example of a modern holistic understanding task, unifying a range of abilities around visually grounded language understanding in a single problem statement. It has also been an early example for which some of the aforementioned issues were identified. To illustrate the new evaluation approach, this thesis introduces ShapeWorld, a diagnostic data generation framework. Its design is guided by the goal to provide a configurable and extensible testbed for the domain of visually grounded language understanding. Based on ShapeWorld data, the strengths and weaknesses of various state-of-the-art visual question answering models are analysed and compared in detail, with respect to their ability to correctly handle statements involving, for instance, spatial relations or numbers. Finally, three case studies illustrate the versatility of this approach and the ShapeWorld generation framework: an investigation of multi-task and curriculum learning, a replication of a psycholinguistic study for deep learning models, and an exploration of a new approach to assess generative tasks like image captioning.Qualcomm Award Premium Research Studentship, Engineering and Physical Sciences Research Council Doctoral Training Studentshi

Question Answering with distilled BERT models: A case study for Biomedical Data

In the healthcare industry today, 80% of data is unstructured (Razzak et al., 2019). The challenge this imposes on healthcare providers is that they rely on unstructured data to inform their decision-making. Although Electronic Health Records (EHRs) exist to integrate patient data, healthcare providers are still challenged with searching for information and answers contained within unstructured data. Prior NLP and Deep Learning research has shown that these methods can improve information extraction on unstructured medical documents. This research expands upon those studies by developing a Question Answering system using distilled BERT models. Healthcare providers can use this system on their local computers to search for and receive answers to specific questions about patients. This paper’s best TinyBERT and TinyBioBERT models had Mean Reciprocal Rank (MRRs) of 0.522 and 0.284 respectively. Based on these findings this paper concludes that TinyBERT performed better than TinyBioBERT on BioASQ task 9b data

Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery

The visual-question localized-answering (VQLA) system can serve as a knowledgeable assistant in surgical education. Except for providing text-based answers, the VQLA system can highlight the interested region for better surgical scene understanding. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning new knowledge. Specifically, when DNNs learn on incremental classes or tasks, their performance on old tasks drops dramatically. Furthermore, due to medical data privacy and licensing issues, it is often difficult to access old data when updating continual learning (CL) models. Therefore, we develop a non-exemplar continual surgical VQLA framework, to explore and balance the rigidity-plasticity trade-off of DNNs in a sequential learning paradigm. We revisit the distillation loss in CL tasks, and propose rigidity-plasticity-aware distillation (RP-Dist) and self-calibrated heterogeneous distillation (SH-Dist) to preserve the old knowledge. The weight aligning (WA) technique is also integrated to adjust the weight bias between old and new tasks. We further establish a CL framework on three public surgical datasets in the context of surgical settings that consist of overlapping classes between old and new surgical VQLA tasks. With extensive experiments, we demonstrate that our proposed method excellently reconciles learning and forgetting on the continual surgical VQLA over conventional CL methods. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability: https://github.com/longbai1006/CS-VQL