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Predicting Machine Translation Comprehension with a Neural Network

Milam Aiken, Jamison Posey, Bart Garner, Brian Reithel
2016 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
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

Fei Wu, Xue Chen, Hongyan Zheng
2022 Wireless Communications and Mobile Computing  
, 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*

Vamsi Krishna Vedantam
2021 Turkish Journal of Computer and Mathematics Education  
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

Jianfeng Gao, Michel Galley, Lihong Li
2018 Proceedings of ACL 2018, Tutorial Abstracts  
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  
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]

Changchang Zeng, Shaobo Li, Qin Li, Jie Hu, Jianjun Hu
2020 arXiv   pre-print
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]

Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li
2019 arXiv   pre-print
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]

Mokanarangan Thayaparan, Marco Valentino, André Freitas
2020 arXiv   pre-print
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

Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah
2022 Proceedings of the ACM Web Conference 2022  
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

Massimo Esposito, Giovanni Luca Masala, Bruno Golosio, Angelo Cangelosi
2020 Frontiers in Robotics and AI  
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

Jianfeng Gao, Michel Galley, Lihong Li
2019 Foundations and Trends in Information Retrieval  
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]

Amit Sahu
2017 arXiv   pre-print
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]

Swapnil Hingmire, Irene Li, Rena Kawamura, Benjamin Chen, Alexander Fabbri, Xiangru Tang, Yixin Liu, Thomas George, Tammy Liao, Wai Pan Wong, Vanessa Yan, Richard Zhou (+2 others)
2021 arXiv   pre-print
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]

Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, Ngoc Thang Vu
2020 arXiv   pre-print
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]

Daria Dzendzik, Carl Vogel, Jennifer Foster
2021 arXiv   pre-print
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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