scholarly journals MTQA: Text-Based Multitype Question and Answer Reading Comprehension Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Deguang Chen ◽  
Ziping Ma ◽  
Lin Wei ◽  
Jinlin Ma ◽  
Yanbin Zhu
Keyword(s):  
Feature Extraction ◽  
Reading Comprehension ◽  
Loss Function ◽  
High Performance ◽  
Question Answering ◽  
Model Performance ◽  
Proposed Model ◽  
Question And Answer ◽  
Research Hotspots ◽  
Better Than

Text-based multitype question answering is one of the research hotspots in the field of reading comprehension models. Multitype reading comprehension models have the characteristics of shorter time to propose, complex components of relevant corpus, and greater difficulty in model construction. There are relatively few research works in this field. Therefore, it is urgent to improve the model performance. In this paper, a text-based multitype question and answer reading comprehension model (MTQA) is proposed. The model is based on a multilayer transformer encoding and decoding structure. In the decoding structure, the headers of the answer type prediction decoding, fragment decoding, arithmetic decoding, counting decoding, and negation are added for the characteristics of multiple types of corpora. Meanwhile, high-performance ELECTRA checkpoints are employed, and secondary pretraining based on these checkpoints and an absolute loss function are designed to improve the model performance. The experimental results show that the performance of the proposed model on the DROP and QUOREF corpora is better than the best results of the current existing models, which proves that the proposed MTQA model has high feature extraction and relatively strong generalization capabilities.

scholarly journals The Compressive Strength of High-Performance Concrete and Ultrahigh-Performance

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
E. H. Kadri ◽  
S. Aggoun ◽  
S. Kenai ◽  
A. Kaci
Keyword(s):  
Experimental Data ◽  
Compressive Strength ◽  
Silica Fume ◽  
High Performance ◽  
High Performance Concrete ◽  
Cementitious Materials ◽  
Strength Gain ◽  
Proposed Model ◽  
Partial Cement Replacement ◽  
Better Than

The compressive strength of silica fume concretes was investigated at low water-cementitious materials ratios with a naphthalene sulphonate superplasticizer. The results show that partial cement replacement up to 20% produce, higher compressive strengths than control concretes, nevertheless the strength gain is less than 15%. In this paper we propose a model to evaluate the compressive strength of silica fume concrete at any time. The model is related to the water-cementitious materials and silica-cement ratios. Taking into account the author's and other researchers’ experimental data, the accuracy of the proposed model is better than 5%.

scholarly journals Supervised attention for answer selection in community question answering

2020 ◽  
Vol 9 (2) ◽  
pp. 203
Author(s):  
Thanh Thi Ha ◽  
Atsuhiro Takasu ◽  
Thanh Chinh Nguyen ◽  
Kiem Hieu Nguyen ◽  
Van Nha Nguyen ◽  
...  
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
Irrelevant Information ◽  
Social Question ◽  
Community Question Answering ◽  
Basic Model ◽  
Proposed Model ◽  
Questions And Answers ◽  
Word Attention ◽  
Better Than

<span class="fontstyle0">Answer selection is an important task in Community Question Answering (CQA). In recent years, attention-based neural networks have been extensively studied in various natural language processing problems, including question answering. This paper explores </span><span class="fontstyle2">matchLSTM </span><span class="fontstyle0">for answer selection in CQA. A lexical gap in CQA is more challenging as questions and answers typical contain multiple sentences, irrelevant information, and noisy expressions. In our investigation, word-by-word attention in the original model does not work well on social question-answer pairs. We propose integrating supervised attention into </span><span class="fontstyle2">matchLSTM</span><span class="fontstyle0">. Specifically, we leverage lexical-semantic from external to guide the learning of attention weights for question-answer pairs. The proposed model learns more meaningful attention that allows performing better than the basic model. Our performance is among the top on SemEval datasets.</span> <br /><br />

scholarly journals Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jingyuan Zhang ◽  
Zequn Zhang ◽  
Zhi Guo ◽  
Li Jin ◽  
Kang Liu ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
Opinion Mining ◽  
Common Knowledge ◽  
Multiple Perspectives ◽  
Fine Grained ◽  
Proposed Model ◽  
Meta Learning ◽  
Benchmark Datasets ◽  
Machine Reading

Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.

scholarly journals Gravitation Theory Based Model for Multi-Label Classification

2017 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Classification Algorithms ◽  
Universal Gravitation ◽  
The Past ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Music Information ◽  
Better Than

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

scholarly journals Intelligent Question Answering System Based on Machine Reading Comprehension

2021 ◽  
Vol 2050 (1) ◽  
pp. 012002
Author(s):  
Qian Shang ◽  
Ming Xu ◽  
Bin Qin ◽  
Pengbin Lei ◽  
Junjian Huang
Keyword(s):  
Artificial Intelligence ◽  
Reading Comprehension ◽  
Loss Function ◽  
Question Answering ◽  
Data Sets ◽  
Question Answering System ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Machine Reading ◽  
Retrieval Phase

Abstract Question answering(Q&A) system is important for accelerating the landing of artificial intelligence. This paper makes an improvement on the Q&A system which uses the method of retrieval-machine reading comprehension (MRC). In the retrieval phase, we use BM25 to recall some documents and split these documents into paragraphs, then we reorder the paragraphs according to the correlation with the question, so as to reduce the number of recalled paragraphs and improve the speed of MRC. In the MRC stage, we design a multi-task MRC structure, which can judge whether the paragraph contains answer and locate answer accurately. Besides, we modify the loss function to fit the sparse labels during the training. The experiments are carried out on multiple data sets to verify the effectiveness of the improved system.

scholarly journals A Compact High-Gain Coplanar Waveguide-Fed Antenna for Military RADAR Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zaheer Ahmed Dayo ◽  
Qunsheng Cao ◽  
Yi Wang ◽  
Sandeep Pirbhulal ◽  
Ali Hassan Sodhro
Keyword(s):  
High Performance ◽  
Coplanar Waveguide ◽  
High Gain ◽  
Geometrical Complexity ◽  
Proposed Model ◽  
Relative Bandwidth ◽  
Field Radiation ◽  
Radar Applications ◽  
Better Than ◽  
Good Agreement

This paper presents a new design of a compact, high-gain coplanar waveguide-fed antenna and proposes a multielement approach to attain enhanced characteristics. The proposed method overcomes the simulation and geometrical complexity and achieves optimal performance features. The antenna prototype is carefully designed, and simulation results have been analyzed. The proposed antenna was fabricated on a new WangLing TP-2 laminate with dimensions (0.195λ × 0.163λ × 0.0052λ) at the lowest resonance of 9.78 GHz. The results have been tested and experimentally verified. The antenna model achieved excellent performance including a peak realized gain better than 9.0 dBi, optimal radiation efficiency better than 87.6% over the operating band, and a good relative bandwidth of 11.48% at 10 dB return loss. Symmetrical stable far-field radiation pattern in orthogonal planes and strong distribution of current are observed. Moreover, a comparative analysis with state-of-the-artwork is presented. The measured and simulation result shows a good agreement. The high-performance antenna results reveal that the proposed model is a good contender of military airborne, land, and naval radar applications.

Advances in imaging SIMS studies of stained and BrdU-labelled human metaphase chromosomes

1995 ◽  
Vol 53 ◽  
pp. 932-933
Author(s):  
R. Levi-Setti ◽  
J. M. Chabala ◽  
R. Espinosa ◽  
M. M. Le Beau
Keyword(s):  
High Performance ◽  
Secondary Ion Mass Spectrometry ◽  
Analytical Sensitivity ◽  
Metaphase Chromosomes ◽  
Banding Patterns ◽  
Sector Mass Spectrometer ◽  
Staining Methods ◽  
The University ◽  
Secondary Ion ◽  
Better Than

We have shown previously that isotope-labelled nucleotides in human metaphase chromosomes can be detected and mapped by imaging secondary ion mass spectrometry (SIMS), using the University of Chicago high resolution scanning ion microprobe (UC SIM). These early studies, conducted with BrdU- and 14C-thymidine-labelled chromosomes via detection of the Br and 28CN- (14C14N-> labelcarrying signals, provided some evidence for the condensation of the label into banding patterns along the chromatids (SIMS bands) reminiscent of the well known Q- and G-bands obtained by conventional staining methods for optical microscopy. The potential of this technique has been greatly enhanced by the recent upgrade of the UC SIM, now coupled to a high performance magnetic sector mass spectrometer in lieu of the previous RF quadrupole mass filter. The high transmission of the new spectrometer improves the SIMS analytical sensitivity of the microprobe better than a hundredfold, overcoming most of the previous imaging limitations resulting from low count statistics.

PENGARUH CONTEXTUAL TEACHING AND LEARNING (CTL) TERHADAP PEMAHAMAN MEMBACA SISWA

2012 ◽  
Vol 18 (1) ◽  
pp. 30
Author(s):  
Anni Holila Pulungan
Keyword(s):  
High School ◽  
Reading Comprehension ◽  
High School Students ◽  
Junior High School ◽  
Teaching And Learning ◽  
Junior High ◽  
School Students ◽  
Lecture Method ◽  
Contextual Teaching ◽  
Better Than

The study deals with the Contextual Teaching and Learning of the students’ reading comprehension at junior high school. Contextual Teaching and Learning is a new alternative for every teachers to relate the materials to the real world. The aims of the research are to analyze the effect of non and CTL method of the students’ reading comprehension.  The research method is an experimental method. The data analysis is taken from the two classess. Then, they divided into two  groups, the control and experimental group. The major findings of the study shows that the effect of Contextual Teaching and Learning on the students’ reading comprehension is better than the non CTL method-lecture method for the junior high school students.

EFFECTS OF LANGUAGE AND PRIOR KNOWLEDGE LEVELS ON HYPERTEXT READING COMPREHENSION

2020 ◽  
Author(s):  
Kshema Jose
Keyword(s):  
Reading Comprehension ◽  
Prior Knowledge ◽  
Language Proficiency ◽  
Language Competence ◽  
Additional Information ◽  
Reading Order ◽  
Low Levels ◽  
Online Texts ◽  
Characteristic Features ◽  
Better Than

<p>This study observed how two hypertext features – absence of a linear or author-specified order and availability of multiple reading aids – influence reading comprehension processes of ESL readers. Studies with native or highly proficient users of English, have suggested that readers reading hypertexts comprehend better than readers reading print texts. This was attributed to (i) presence of hyperlinks that provide access to additional information that can potentially help overcome comprehension obstacles and (ii) the absence of an author-imposed reading order that helps readers exercise cognitive flexibility. An aspect that remains largely un-researched is how well readers with low language competence comprehend hypertexts. This research sought to initiate research in the area by exploring the question: Do all ESL readers comprehend a hypertext better than a print text?</p> <p>Keeping in mind the fact that a majority of readers reading online texts in English can be hindered by three types of comprehension deficits – low levels of language proficiency, non-availability of prior knowledge, or both – this study investigated how two characteristic features of hypertext, viz., linking to additional information and non-linearity in presentation of information, affect reading comprehension of ESL readers. </p> <p>Two types of texts that occur in the electronic medium – linear or pre-structured texts and non-linear or self-navigating texts, were used in this study. Based on a comparison of subjects’ comprehension outcomes and free recalls, text factors and reader factors that can influence hypertext reading comprehension of ESL readers are identified. </p> Contradictory to what many researchers believe, results indicate that self-navigating hypertexts might not promote deep comprehension in all ESL readers.

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