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Measuring Beginner Friendliness of Japanese Web Pages explaining Academic Concepts by Integrating Neural Image Feature and Text Features 结合神经图像特征和文本特征测量日语学术概念网页的初学者友好度
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3721
Hayato Shiokawa, K. Kawaguchi, Bingcai Han, T. Utsuro, Yasuhide Kawada, Masaharu Yoshioka, N. Kando
{"title":"Measuring Beginner Friendliness of Japanese Web Pages explaining Academic Concepts by Integrating Neural Image Feature and Text Features","authors":"Hayato Shiokawa, K. Kawaguchi, Bingcai Han, T. Utsuro, Yasuhide Kawada, Masaharu Yoshioka, N. Kando","doi":"10.18653/v1/W18-3721","DOIUrl":"https://doi.org/10.18653/v1/W18-3721","url":null,"abstract":"Search engine is an important tool of modern academic study, but the results are lack of measurement of beginner friendliness. In order to improve the efficiency of using search engine for academic study, it is necessary to invent a technique of measuring the beginner friendliness of a Web page explaining academic concepts and to build an automatic measurement system. This paper studies how to integrate heterogeneous features such as a neural image feature generated from the image of the Web page by a variant of CNN (convolutional neural network) as well as text features extracted from the body text of the HTML file of the Web page. Integration is performed through the framework of the SVM classifier learning. Evaluation results show that heterogeneous features perform better than each individual type of features.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125070140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid 语音学习辅助工具中推荐和反馈功能的重要性
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3711
D. Fudholi, H. Suominen
{"title":"The Importance of Recommender and Feedback Features in a Pronunciation Learning Aid","authors":"D. Fudholi, H. Suominen","doi":"10.18653/v1/W18-3711","DOIUrl":"https://doi.org/10.18653/v1/W18-3711","url":null,"abstract":"Verbal communication — and pronunciation as its part — is a core skill that can be developed through guided learning. An artificial intelligence system can take a role in these guided learning approaches as an enabler of an application for pronunciation learning with a recommender system to guide language learners through exercises and feedback system to correct their pronunciation. In this paper, we report on a user study on language learners’ perceived usefulness of the application. 16 international students who spoke non-native English and lived in Australia participated. 13 of them said they need to improve their pronunciation skills in English because of their foreign accent. The feedback system with features for pronunciation scoring, speech replay, and giving a pronunciation example was deemed essential by most of the respondents. In contrast, a clear dichotomy between the recommender system perceived as useful or useless existed; the system had features to prompt new common words or old poorly-scored words. These results can be used to target research and development from information retrieval and reinforcement learning for better and better recommendations to speech recognition and speech analytics for accent acquisition.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122699315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring 基于深度卷积递归神经网络的文本定性特征增强论文自动评分
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3713
Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha
{"title":"Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring","authors":"Tirthankar Dasgupta, Abir Naskar, Lipika Dey, Rupsa Saha","doi":"10.18653/v1/W18-3713","DOIUrl":"https://doi.org/10.18653/v1/W18-3713","url":null,"abstract":"In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and sentence representation of a text, we try to augment the different complex linguistic, cognitive and psycological features associated within a text document along with a hierarchical convolution recurrent neural network framework. Our preliminary investigation shows that incorporation of such qualitative feature vectors along with standard word/sentence embeddings can give us better understanding about improving the overall evaluation of the input essays.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129878278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 33
Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis NLPTEA-2018共享任务汉语语法错误诊断概述
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3706
Gaoqi Rao, Qi Gong, Baolin Zhang, Endong Xun
{"title":"Overview of NLPTEA-2018 Share Task Chinese Grammatical Error Diagnosis","authors":"Gaoqi Rao, Qi Gong, Baolin Zhang, Endong Xun","doi":"10.18653/v1/W18-3706","DOIUrl":"https://doi.org/10.18653/v1/W18-3706","url":null,"abstract":"This paper presents the NLPTEA 2018 shared task for Chinese Grammatical Error Diagnosis (CGED) which seeks to identify grammatical error types, their range of occurrence and recommended corrections within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 20 teams registered for this shared task, 13 teams developed the system and submitted a total of 32 runs. Progress in system performances was obviously, reaching F1 of 36.12% in position level and 25.27% in correction level. All data sets with gold standards and scoring scripts are made publicly available to researchers.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125460547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 42
Contextualized Character Representation for Chinese Grammatical Error Diagnosis 语境化汉字表征在汉语语法错误诊断中的应用
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3725
Jianbo Zhao, Si Li, Zhiqing Lin
{"title":"Contextualized Character Representation for Chinese Grammatical Error Diagnosis","authors":"Jianbo Zhao, Si Li, Zhiqing Lin","doi":"10.18653/v1/W18-3725","DOIUrl":"https://doi.org/10.18653/v1/W18-3725","url":null,"abstract":"Nowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ling@CASS Solution to the NLP-TEA CGED Shared Task 2018 Ling@CASS NLP-TEA CGED共享任务2018解决方案
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3709
Q. Hu, Yongwei Zhang, Fang Liu, Yueguo Gu
{"title":"Ling@CASS Solution to the NLP-TEA CGED Shared Task 2018","authors":"Q. Hu, Yongwei Zhang, Fang Liu, Yueguo Gu","doi":"10.18653/v1/W18-3709","DOIUrl":"https://doi.org/10.18653/v1/W18-3709","url":null,"abstract":"In this study, we employ the sequence to sequence learning to model the task of grammar error correction. The system takes potentially erroneous sentences as inputs, and outputs correct sentences. To breakthrough the bottlenecks of very limited size of manually labeled data, we adopt a semi-supervised approach. Specifically, we adapt correct sentences written by native Chinese speakers to generate pseudo grammatical errors made by learners of Chinese as a second language. We use the pseudo data to pre-train the model, and the CGED data to fine-tune it. Being aware of the significance of precision in a grammar error correction system in real scenarios, we use ensembles to boost precision. When using inputs as simple as Chinese characters, the ensembled system achieves a precision at 86.56% in the detection of erroneous sentences, and a precision at 51.53% in the correction of errors of Selection and Missing types.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"514 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123567592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Short Answer Grading System in Chinese by Support Vector Approach 基于支持向量法的中文简答评分系统
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3718
Shih-Hung Wu, Wen-Feng Shih
{"title":"A Short Answer Grading System in Chinese by Support Vector Approach","authors":"Shih-Hung Wu, Wen-Feng Shih","doi":"10.18653/v1/W18-3718","DOIUrl":"https://doi.org/10.18653/v1/W18-3718","url":null,"abstract":"In this paper, we report a short answer grading system in Chinese. We build a system based on standard machine learning approaches and test it with translated corpus from two publicly available corpus in English. The experiment results show similar results on two different corpus as in English.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"166 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
MULLE: A grammar-based Latin language learning tool to supplement the classroom setting 一个以语法为基础的拉丁语学习工具，作为课堂教学的补充
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3715
Herbert Lange, Peter Ljunglöf
{"title":"MULLE: A grammar-based Latin language learning tool to supplement the classroom setting","authors":"Herbert Lange, Peter Ljunglöf","doi":"10.18653/v1/W18-3715","DOIUrl":"https://doi.org/10.18653/v1/W18-3715","url":null,"abstract":"MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"397 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection 统计知识与条件随机场相结合的汉语语法错误检测方法
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3728
Yiyi Wang, Chilin Shih
{"title":"A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection","authors":"Yiyi Wang, Chilin Shih","doi":"10.18653/v1/W18-3728","DOIUrl":"https://doi.org/10.18653/v1/W18-3728","url":null,"abstract":"This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL). We describe the architecture of the model and its performance in the shared task of the ACL 2018 Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA). This hybrid approach yields comparably high false positive rate (FPR = 0.1274) and precision (Pd= 0.7519; Pi= 0.6311), but low recall (Rd = 0.3035; Ri = 0.1696 ) in grammatical error detection and identification tasks. Additional statistical information and linguistic rules can be added to enhance the model performance in the future.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134066599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction 汉语语法错误诊断与纠错的混合系统
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3708
Chen Li, Junpei Zhou, Zuyi Bao, Hengyou Liu, Guangwei Xu, Linlin Li
{"title":"A Hybrid System for Chinese Grammatical Error Diagnosis and Correction","authors":"Chen Li, Junpei Zhou, Zuyi Bao, Hengyou Liu, Guangwei Xu, Linlin Li","doi":"10.18653/v1/W18-3708","DOIUrl":"https://doi.org/10.18653/v1/W18-3708","url":null,"abstract":"This paper introduces the DM_NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
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