Stanford Question Answering Dataset Research Articles

Comparative Analysis of State-of-the-Art Q A Models: BERT, RoBERTa, DistilBERT, and ALBERT on SQuAD v2 Dataset

In the rapidly evolving landscape of natural language processing (NLP) and artificial intelligence, recent years have witnessed significant advancements, particularly in text-based question-answering (QA) systems. The Stanford Question Answering Dataset (SQuAD v2) has emerged as a prominent benchmark, offering diverse language understanding challenges. This study conducts a thorough examination of cutting-edge QA models—BERT, DistilBERT, RoBERTa, and ALBERT—each featuring distinct architectures, focusing on their training and performance on SQuAD v2. The analysis aims to uncover the unique strengths of each model, providing insights into their capabilities and exploring the impact of different training techniques on their performance. The primary objective is to enhance our understanding of text-based QA systems' evolution and their effectiveness in real-world scenarios. The results of this comparative study are poised to influence the utilization and development of these models in both industry and research. The investigation meticulously evaluates BERT, ALBERT, RoBERTa, and DistilBERT QA models using the SQuAD v2 dataset, emphasizing instances of accurate responses and identifying areas where completeness may be lacking. This nuanced exploration contributes to the ongoing discourse on the advancement of text-based question-answering systems, shedding light on the strengths and limitations of each QA model. Based on the results obtained, ALBERT achieved an exact match of 86.85% and an F1 score of 89.91% on the SQuAD v2 dataset, demonstrating superior performance in both answerable ('HasAns') and unanswerable ('NoAns') questions. BERT and RoBERTa also showed strong performance, while DistilBERT lagged slightly behind. This study provides a significant contribution to the advancement of text-based question-answering systems, offering insights that can shape the utilization of these models in both industry and research domains.

Question Answering System Based on Bideirectional Long-Short-Term Memory (Bilstm)

In the modern world, Q&A systems are essential for promoting better communication between people and technology. These systems play an important role in collecting information quickly and efficiently, and this leads to great progress in learning, teaching and development in many areas of life. Using deep learning techniques, this research addresses the problem of excellent prediction of the questions that need to be answered. We created a question-and-answer system using "Bidirectional Long Short-Term Memory (BiLSTM)", a modern neural network known for its accuracy and results in text analysis and natural language understanding. This technique is more effective in understanding questions and producing very accurate answers because of its special ability to pay attention to preceding and following information in a sentence. Preprocessing was used to remove unnecessary, unimportant and time-consuming data. The "Stanford Question Answering Dataset version 2 (SQuAD 2.0)"was used, which is considered one of the important datasets used in the field of machine learning and natural language processing. The following evaluation metrics were used to evaluate the model’s performance: “Mean Average Precision(MAP), Mean Reciprocal Rank(MRR), Recall, Precision, Loss, F1 Score, and Exact Match (EM). The results, based on 150 epochs (EPOGs) and 128 batch sizes with a cleaned dataset split into 70% training and 30% test/validation (15% each), are as follows:"Precision (0.966), Loss (0.591), F1-score (0.966), Recall (0.967), EM (0.967), MRR(0.918), MAP (0.776), and accuracy(0.966)". Interestingly, the highest performance was observed when using the accuracy measure.

Experimental Design of Extractive Question-Answering Systems: Influence of Error Scores and Answer Length

Question-answering (QA) systems are becoming more and more important because they enable human-computer communication in a natural language. In recent years, significant progress has been made with transformer-based models that leverage deep learning in combination with large amounts of text data. However, a significant challenge with QA systems lies in their complexity rooted in the ambiguity and flexibility of a natural language. This makes even their evaluation a formidable task. For this reason, in this study, we focus on the evaluation of extractive question-answering (EQA) systems by conducting a large-scale analysis of distilBERT using benchmark data provided by the Stanford Question Answering Dataset (SQuAD). Specifically, the main objectives of this paper are fourfold. First, we study the influence of the answer length on the performance and we demonstrate that there is an inverse correlation between both. Second, we study differences in exact match (EM) measures because there are different definitions commonly used in the literature. As a result, we find that despite the fact that all of those measures are named ”exact match” these measures are actually different from each other. Third, we study the practical relevance of these different definitions because due to the ambivalent meaning of ”exact match” in the literature, it is often unclear if reported improvements are genuine or only due to a change in the exact match measure. Importantly, our results show that differences between differently defined EM measures are in the same order of magnitude as reported differences found in the literature. This raises concerns about the robustness of reported results. Fourth, we provide guidelines to improve the experimental design of general EQA studies, aiming to enhance performance evaluation and minimize the potential for spurious results.

NEUROSYNTH: Enhancing Cognitive Computing using Deep Neural Networks

This study presents an innovative approach to enhance question-answering (QA) systems that utilize a RoBERTa-based architecture and complexity-enhanced input features. The work is divided into four primary parts: training methodology, feature engineering, building models, and data preprocessing. We propose a Python function that uses readability measures and natural language processing techniques to calculate the linguistic difficulty metrics for input sentences. TensorFlow Datasets (TFDS) are then used to load and preprocess the SquAD (Stanford Question Answering Dataset) dataset to enable effective training. Word embeddings from previously trained GloVe vectors are integrated with complexity metrics to prepare input features that add contextual information to the input representation. With the inclusion of the enriched features, a distinctive question-answering model based on the RoBERTa architecture is trained using the AdamW optimizer and CrossEntropyLoss. Iterative epochs are used in the training process to optimize the model's parameters and minimize the loss function. An independent validation dataset is used to evaluate the model's performance, proving the usefulness of the suggested method in improving the accuracy and robustness of the QA system. All in all, this work offers an organized strategy for improving the quality of systems by fusing cutting-edge neural architecture with input properties that are increased by complexity.

Feasibility of Using Zero-Shot Learning in Transformer-Based Natural Language Processing Algorithm for Key Information Extraction from Head and Neck Tumor Board Notes

Natural language processing (NLP) technology has the potential to automate information aggregation and summarization in oncology. One example is the automation of patient registry creation. In this work, we aim to show (1) the feasibility of using modern NLP algorithms to extract key information from tumor board notes, and (2) the impact of prompt engineering on the quality of the results. In this IRB-approved study, we obtained the texts of head and neck tumor board notes for 306 unique patients. Five key pieces of information used to create a patient registry were predefined: age, gender, tumor histology, tumor stage, and primary location. The NLP algorithm used was a modified Text-To-Text Transfer Transformer (T5) model that was initially trained on the Colossal Clean Crawled Corpus (C4) dataset and subsequently fine-tuned on the Stanford Question Answering Dataset (SQuAD) to perform the downstream task of extractive question answering. The NLP model and trained weights were obtained from the Hugging Face platform. During inference, the entire body of the tumor board note and a related question were fed as inputs, and the model predicted a sequence of texts in response to the question. Two sets of questions of similar semantic meanings were used. Questions in prompt set #1 included "What is the gender?", "What is the age?", "What is the type of carcinoma in pathological diagnosis?", "What is the stage?", and "Where is the carcinoma located at?". Questions in prompt set #2 include "Is the patient male or female?", "How old is the patient?", "What kind of cancer?", "What is the cancer stage?", and "What is the tumor location?". Each model-predicted response was compared to the ground truth extracted from the tumor board notes. A response was classified as true if it is consistent with the ground truth, otherwise, it was deemed false. The response accuracy for each question was subsequently calculated. The median number of words in each tumor board note was 448 (range, 219 - 1505). The accuracy of the NLP algorithm for each question from either set is reported in Table 1. Algorithm performance is higher for extracting objective information such as age, gender, and histology. In addition, it was found that questions of similar semantic meanings but with different wording can lead to significantly different results. We demonstrated that a transformer-based extractive question-answering NLP algorithm can be successfully used for extracting information from head and neck tumor board notes with zero-shot learning. Furthermore, our results highlight the significance of prompt engineering for applying NLP for this task. Future work on finetuning these algorithms to oncology-specific texts can potentially enhance algorithm performance for more difficult tasks.

Multigranularity Syntax Guidance with Graph Structure for Machine Reading Comprehension

In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-based MRC models (for example, restrictions on word count), such models cannot effectively integrate significant features, such as syntax relations, semantic connections, and long-distance semantics between sentences, leading to the inability of the available models to better understand the intrinsic connections between text and questions to be answered based on it. In this paper, a multi-granularity syntax guidance (MgSG) module that consists of a “graph with dependence” module and a “graph with entity” module is proposed. MgSG selects both sentence and word granularities to guide the text model to decipher the text. In particular, syntactic constraints are used to guide the text model while exploiting the global nature of graph neural networks to enhance the model’s ability to construct long-range semantics. Simultaneously, named entities play an important role in text and answers and focusing on entities can improve the model’s understanding of the text’s major idea. Ultimately, fusing multiple embedding representations to form a representation yields the semantics of the context and the questions. Experiments demonstrate that the performance of the proposed method on the Stanford Question Answering Dataset is better when compared with the traditional BERT baseline model. The experimental results illustrate that our proposed “MgSG” module effectively utilizes the graph structure to learn the internal features of sentences, solve the problem of long-distance semantics, while effectively improving the performance of PrLM in machine reading comprehension.

FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning

Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to “understand” a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.

Strategies for Improving Text Reading Ability Based on Human-Computer Interaction in Artificial Intelligence.

In order to improve text reading ability, a human-computer interaction method based on artificial intelligence (AI) human-computer interaction is proposed. Firstly, the design of the AI human-computer interaction model is constructed, which includes the Stanford Question Answering Dataset (SQuAD) and the designed baseline model. There are three components: the coding layer is based on a cyclic neural network (recurrent neural network [RNN] encoder layer), which aims to encode the problem and text into a hidden state; the interaction layer is used to integrate problems and text representation; the output layer connects two independent soft Max layers after a fully connected layer, one is used to obtain the starting position of the answer in the text and the other is used to obtain the ending position. In the interaction layer of the model, this manuscript uses hierarchical attention and aggregation mechanism to improve text coding. The traditional model interaction layer has a simple structure, which leads to weak relevance between text and problems, and poor understanding ability of the model. Finally, the self-attention model is used to further enhance the feature representation of text. The experimental results show that the improved model in this manuscript is compared with the public AI human-computer interaction reading comprehension model. According to the data in the table, the accuracy of the model in this manuscript is better than that of the baseline model, in which the exact match (EM) value is increased by 1.4% and the F1 value is increased by 2.7%. However, compared with improvement point 2, the EM and F1 values of the model have decreased by 0.7%. It shows that the output layer has a certain impact on the effect of the model, and the improvement and optimization of the output layer can also improve the performance of the model. It is proved that AI human-computer interaction can effectively improve text reading ability.

You Don’t Need Labeled Data for Open-Book Question Answering

Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset—SQuAD and Natural Questions datasets. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.

Automatic Taxonomy Classification by Pretrained Language Model

In recent years, automatic ontology generation has received significant attention in information science as a means of systemizing vast amounts of online data. As our initial attempt of ontology generation with a neural network, we proposed a recurrent neural network-based method. However, updating the architecture is possible because of the development in natural language processing (NLP). By contrast, the transfer learning of language models trained by a large, unlabeled corpus has yielded a breakthrough in NLP. Inspired by these achievements, we propose a novel workflow for ontology generation comprising two-stage learning. Our results showed that our best method improved accuracy by over 12.5%. As an application example, we applied our model to the Stanford Question Answering Dataset to show ontology generation in a real field. The results showed that our model can generate a good ontology, with some exceptions in the real field, indicating future research directions to improve the quality.

GSA-Net: gated scaled dot-product attention based neural network for reading comprehension

Reading Comprehension (RC) is concerned with building systems that automatically answer questions about a given context passage. The interactions between the context and question are very important to locate the correct answer. In this paper, we propose a Gated Scaled Dot-Product Attention based model for RC task. The character-level embedding is incorporated into the word embedding which is helpful to deal with Out-of-Vocabulary (OOV) tokens. The attention distribution is obtained by scaled dot product which captures the interaction between question and passage effectively. Further, self-matching attention mechanism is adopted to resolve the problem of long-distance dependency. These components provides more information for the prediction of the starting and ending position of the answer. We evaluate our method on Stanford Question Answering Dataset (SQuAD) and the results show that different components in the model boost the performance.

Rception: Wide and Deep Interaction Networks for Machine Reading Comprehension (Student Abstract)

Most of models for machine reading comprehension (MRC) usually focus on recurrent neural networks (RNNs) and attention mechanism, though convolutional neural networks (CNNs) are also involved for time efficiency. However, little attention has been paid to leverage CNNs and RNNs in MRC. For a deeper understanding, humans sometimes need local information for short phrases, sometimes need global context for long passages. In this paper, we propose a novel architecture, i.e., Rception, to capture and leverage both local deep information and global wide context. It fuses different kinds of networks and hyper-parameters horizontally rather than simply stacking them layer by layer vertically. Experiments on the Stanford Question Answering Dataset (SQuAD) show that our proposed architecture achieves good performance.

Latent Question Interpretation Through Variational Adaptation

Most artificial neural network models for question-answering rely on complex attention mechanisms. These techniques demonstrate high performance on existing datasets; however, they are limited in their ability to capture natural language variability, and to generate diverse relevant answers. To address this limitation, we propose a model that learns multiple interpretations of a given question. This diversity is ensured by our interpretation policy module which automatically adapts the parameters of a question-answering model with respect to a discrete latent variable. This variable follows the distribution of interpretations learned by the interpretation policy through a semi-supervised variational inference framework. To boost the performance further, the resulting policy is fine-tuned using the rewards from the answer accuracy with a policy gradient. We demonstrate the relevance and efficiency of our model through a large panel of experiments. Qualitative results, in particular, underline the ability of the proposed architecture to discover multiple interpretations of a question. When tested using the Stanford Question Answering Dataset 1.1, our model outperforms the baseline methods in finding multiple and diverse answers. To assess our strategy from a human standpoint, we also conduct a large-scale user study. This study highlights the ability of our network to produce diverse and coherent answers compared to existing approaches. Our Pytorch implementation is available as open source. 1 1 github.com/parshakova/APIP .

GF-Net: Improving machine reading comprehension with feature gates

Machine reading comprehension (MRC) is a field of question-answering in which computers understand given passages and answer related questions. Several previous models have tried to combine the use of linguistic and word embedding features to improve the performance of MRC; however, they could not obtain successful results because of feature interference problems caused by simple concatenation of the two. To resolve these problems, a machine reading comprehension model called gated feature network (GF-Net) is proposed in which linguistic features are selectively used according to their roles in the process of answer selection. In the GF-Net, the weights of the linguistic features are automatically controlled through gate mechanisms called feature gates. In the experiments with Stanford Question Answering Dataset SQuAD, the MRC models with feature gates showed a 0.67%p higher average of exact match (EM) and 0.64%p higher average of F1-score than models without feature gates. In addition, the GF-Net outperformed the previous MRC models to which feature gates were added. Based on these experimental results, it is concluded that the gate mechanism can contribute to an improvement in the performance of MRC models and the architecture of the GF-Net is suitable for the task of MRC.

An Approach Towards Question Answering Using Apposite Context

Question Answering (QA) is concerned with building systems that automatically answer questions given by humans in a natural language. Most of the successful approaches for these systems rely on Recurrent Neural Networks (RNNs). But, running them over long documents is prohibitively slow. Traditional models for QA optimize using cross entropy loss on the start and end positions of the answer. But, these models do not consider word overlapping between actual and predicted ones and thus, may results in wrong output. The proposed system puts forward a QA model that combines cross entropy loss with word overlapping. Another significance is that, the context being fed into the QA model is shortened by selecting only the relevant sentences with respect to the input question, from the source document. The model is implemented with the aid of a mechanism called Dynamic Coattention Network (DCN). Dataset used for the implementation of this system is Stanford Question Answering Dataset (SQuAD).