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Predicting Machine Translation Comprehension with a Neural Network
2016
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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In the current study, we employ an artificial neural network to analyze survey responses and reading test scores, resulting in a significantly correlated forecast of reading comprehension. ...
Prior studies have had mixed results in predicting which variables have the greatest influence on translation comprehension. ...
First, only one short reading comprehension test and one Cloze test were administered. ...
doi:10.24297/ijct.v15i2.3980
fatcat:uglm2psv4bfj3e5aixkfw2ukq4
CET-4 Listening Test Effect on Listening Learning Based on Machine Learning
2022
Wireless Communications and Mobile Computing
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, including support vector machines, principal component analysis, statistical learning methods, and BP neural network. ...
This paper conducted an empirical study on the effect of the machine learning-based CET-4 listening test on listening learning. ...
The written test consists of four parts: listening comprehension, reading comprehension, writing, and translation. ...
doi:10.1155/2022/5604032
doaj:a378c01329e8450884958548db798a05
fatcat:joxmbkbgdrhvlltwzga7wwimmm
The Survey: Advances in Natural Language Processing using Deep Learning*
2021
Turkish Journal of Computer and Mathematics Education
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Natural Language Processing using Deep Learning is one of the critical areas of Artificial Intelligence to focus in the next decades. ...
The latest developments in Natural Language Processing con- tributed to the successful implementation of machine translations, linguistic models, Speech recognitions, automatic text generations applications ...
Machine reading comprehension: One of the key areas of advancements in NLP is machine's ability to read the text and extract meaning and context out of it. ...
doi:10.17762/turcomat.v12i4.611
fatcat:f35ilyn3p5elvgnd5k2jecmqiy
Neural Approaches to Conversational AI
2018
Proceedings of ACL 2018, Tutorial Abstracts
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This tutorial surveys neural approaches to conversational AI that were developed in the last few years. ...
For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and ...
We then discuss neural text-QA agents. The heart of such systems is a neural Machine Reading Comprehension (MRC) model that generates an answer to an input query based on a set of passages. ...
doi:10.18653/v1/p18-5002
dblp:conf/acl/GaoGL18
fatcat:7llxwuntafh4fcjj4ia3tm642a
Machine Reading Comprehension: Methods and Trends of Low Resource Languages
2021
jecet
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This study presents a survey on trends and methods of Machine Reading Comprehension (MRC) in these low-resource languages. ...
Several studies on Machine Reading Comprehension (MRC) have proposed MRC models based on English. Nonetheless, these models provide insignificant performance on low-resource languages. ...
This study's primary purpose is to present a survey on Machine Reading Comprehension methods, particularly on low resource language. ...
doi:10.24214/jecet.b.10.2.05775
fatcat:fto7t3avmzexzn6nfsuu4k2emm
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
[article]
2020
arXiv
pre-print
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Machine Reading Comprehension (MRC) is a challenging Natural Language Processing(NLP) research field with wide real-world applications. ...
At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension ...
Conclusions We conducted a comprehensive survey of recent efforts on the tasks, evaluation metrics, and benchmark datasets of machine reading comprehension (MRC). ...
arXiv:2006.11880v2
fatcat:auup4gvsuzf4dkjb2n6nzyfl3m
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
[article]
2019
arXiv
pre-print
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We collect, select, summarize, discuss and analyze these works in a comprehensive way andcover all the related information to make the article self-contained. ...
Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic. ...
Contributions of this survey. The aim of this survey is to provide a comprehensive review on the research efforts on generating adversarial examples on textual deep neural networks. ...
arXiv:1901.06796v3
fatcat:gfh4gzkvn5djpdkn7k63xlqahm
A Survey on Explainability in Machine Reading Comprehension
[article]
2020
arXiv
pre-print
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This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). ...
of explainability in Machine Reading Comprehension. ...
Figure 2 : 2 Explainable Machine Reading Comprehension (MRC) approaches. ...
arXiv:2010.00389v1
fatcat:jzxjysnma5ee5auvplfxxfar2u
ExpScore: Learning Metrics for Recommendation Explanation
2022
Proceedings of the ACM Web Conference 2022
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To measure explainability in a comprehensive and interpretable manner, we propose ExpScore, a novel machine learning-based metric that incorporates the definition of explainability from various perspectives ...
Many information access and machine learning systems, including recommender systems, lack transparency and accountability. ...
Figure 1 : 1 Figure 1: Recommendation explanation survey. MTurkers first read the description of a given item and then rate three explanations from 1 to 5. ...
doi:10.1145/3485447.3512269
fatcat:qiwfym2y6zfohfjvoye7corf3i
Editorial: Language Representation and Learning in Cognitive and Artificial Intelligence Systems
2020
Frontiers in Robotics and AI
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Miyazawa et al. presents a unified framework, integrating a cognitive architecture in a real robot for the simultaneously comprehension of concepts, actions, and language. ...
Ferrone and Zanzotto describe a survey aimed to deeply investigate the link between symbolic and distributed/distributional representations of Natural Language. ...
AUTHOR CONTRIBUTIONS All authors contributed equally to manuscript writing, read, and approved the final version. ...
doi:10.3389/frobt.2020.00069
pmid:33501236
pmcid:PMC7807395
fatcat:ue2g2s3sunfbtgij7ydfuvqfhy
Neural Approaches to Conversational AI
2019
Foundations and Trends in Information Retrieval
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neural dialogue agents. • Chapter 3 describes question answering (QA) agents, focusing on neural models for knowledge-base QA and machine reading comprehension (MRC). • Chapter 4 describes task-oriented ...
As Fig. 1.4 illustrates, neural methods in NLP tasks (e.g., machine reading comprehension and dialogue) often consist of three steps: (1) encoding symbolic user input and knowledge into their neural ...
doi:10.1561/1500000074
fatcat:5ou22zmnq5ghjnkqmbxbfkurhu
Survey of reasoning using Neural networks
[article]
2017
arXiv
pre-print
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Most prominent neural architectures for such tasks are Memory networks: inference components combined with long term memory and Neural Turing Machines: neural networks using external memory resources. ...
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve small memory contexts, but as context becomes larger than a threshold, it is difficult to use them. ...
Approaches
Neural Turing Machines Architecture of Neural Turing Machine (NTM) [5] contains mainly: a neural network controller and a memory bank. ...
arXiv:1702.06186v2
fatcat:y6btabr3qrf3lnxbete44tfxym
CLICKER: A Computational LInguistics Classification Scheme for Educational Resources
[article]
2021
arXiv
pre-print
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We propose a classification scheme -- CLICKER for CL/NLP based on the analysis of online lectures from 77 university courses on this subject. ...
We observed that a comprehensive classification system like CCS or Mathematics Subject Classification (MSC) does not exist for Computational Linguistics (CL) and Natural Language Processing (NLP). ...
Analysis
Generating surveys from web resources on wikipedia-
Methods in Neural Language Processing: A Survey. ...
arXiv:2112.08578v1
fatcat:flggtjmvlraa3bvw2jxol4d6re
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2010.06396v1 fatcat:uns4kjgpnrepffpppyizbsxkf4
In this paper, we propose a new method that leverages eye-tracking data to investigate the relationship between human visual attention and neural attention in machine reading comprehension. ...
To this end, we introduce a novel 23 participant eye tracking dataset - MQA-RC, in which participants read movie plots and answered pre-defined questions. ...
To the best of our knowledge, we are the first to propose a systematic approach for comparing neural attention to human gaze data in machine reading comprehension. ...
arXiv:2010.06396v2
fatcat:y5gmfilu2rh2bczrkqnls5sfla
English Machine Reading Comprehension Datasets: A Survey
[article]
2021
arXiv
pre-print
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This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem. ...
Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets. ...
A survey on neural machine reading compre-
hension. arXiv:1906.03824.
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018.
Know what you don't know: Unanswerable ques-
tions for SQuAD. ...
arXiv:2101.10421v2
fatcat:xdkiczo3zzdclgbwpxgamvtgwm
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