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Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration 移动教育活动中多语种短文本响应聚类的初步探索
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3723
Yuen-Hsien Tseng, Lung-Hao Lee, Y. Chien, Chun-Yen Chang, T. Li
{"title":"Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration","authors":"Yuen-Hsien Tseng, Lung-Hao Lee, Y. Chien, Chun-Yen Chang, T. Li","doi":"10.18653/v1/W18-3723","DOIUrl":"https://doi.org/10.18653/v1/W18-3723","url":null,"abstract":"Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"34 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":"123487343","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: 3
Generating Questions for Reading Comprehension using Coherence Relations 运用连贯关系为阅读理解生成问题
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3701
Takshak Desai, Parag Dakle, D. Moldovan
{"title":"Generating Questions for Reading Comprehension using Coherence Relations","authors":"Takshak Desai, Parag Dakle, D. Moldovan","doi":"10.18653/v1/W18-3701","DOIUrl":"https://doi.org/10.18653/v1/W18-3701","url":null,"abstract":"In this paper, we have proposed a technique for generating complex reading comprehension questions from a discourse that are more useful than factual ones derived from assertions. Our system produces a set of general-level questions using coherence relations and a set of well-defined syntactic transformations on the input text. Generated questions evaluate comprehension abilities like a comprehensive analysis of the text and its structure, correct identification of the author’s intent, a thorough evaluation of stated arguments; and a deduction of the high-level semantic relations that hold between text spans. Experiments performed on the RST-DT corpus allow us to conclude that our system possesses a strong aptitude for generating intricate questions. These questions are capable of effectively assessing a student’s interpretation of the text.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"40 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":"122626675","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: 11
From Fidelity to Fluency: Natural Language Processing for Translator Training 从忠实到流利:翻译培训的自然语言处理
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3719
O. Kwong
{"title":"From Fidelity to Fluency: Natural Language Processing for Translator Training","authors":"O. Kwong","doi":"10.18653/v1/W18-3719","DOIUrl":"https://doi.org/10.18653/v1/W18-3719","url":null,"abstract":"This study explores the use of natural language processing techniques to enhance bilingual lexical access beyond simple equivalents, to enable translators to navigate along a wider cross-lingual lexical space and more examples showing different translation strategies, which is essential for them to learn to produce not only faithful but also fluent translations.","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":"114447654","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
Countering Position Bias in Instructor Interventions in MOOC Discussion Forums 应对MOOC论坛讲师干预中的立场偏见
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3720
Muthu Kumar Chandrasekaran, Min-Yen Kan
{"title":"Countering Position Bias in Instructor Interventions in MOOC Discussion Forums","authors":"Muthu Kumar Chandrasekaran, Min-Yen Kan","doi":"10.18653/v1/W18-3720","DOIUrl":"https://doi.org/10.18653/v1/W18-3720","url":null,"abstract":"We systematically confirm that instructors are strongly influenced by the user interface presentation of Massive Online Open Course (MOOC) discussion forums. In a large scale dataset, we conclusively show that instructor interventions exhibit strong position bias, as measured by the position where the thread appeared on the user interface at the time of intervention. We measure and remove this bias, enabling unbiased statistical modelling and evaluation. We show that our de-biased classifier improves predicting interventions over the state-of-the-art on courses with sufficient number of interventions by 8.2% in F1 and 24.4% in recall on average.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"52 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":"123773718","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
Thank “Goodness”! A Way to Measure Style in Student Essays 谢谢“善良”!一种衡量学生作文风格的方法
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3705
Sandeep Albert Mathias, P. Bhattacharyya
{"title":"Thank “Goodness”! A Way to Measure Style in Student Essays","authors":"Sandeep Albert Mathias, P. Bhattacharyya","doi":"10.18653/v1/W18-3705","DOIUrl":"https://doi.org/10.18653/v1/W18-3705","url":null,"abstract":"Essays have two major components for scoring - content and style. In this paper, we describe a property of the essay, called goodness, and use it to predict the score given for the style of student essays. We compare our approach to solve this problem with baseline approaches, like language modeling and also a state-of-the-art deep learning system. We show that, despite being quite intuitive, our approach is very powerful in predicting the style of the essays.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"113 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":"127154844","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: 7
A Tutorial Markov Analysis of Effective Human Tutorial Sessions 有效人类教程会话的教程马尔可夫分析
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3704
Nabin Maharjan, V. Rus
{"title":"A Tutorial Markov Analysis of Effective Human Tutorial Sessions","authors":"Nabin Maharjan, V. Rus","doi":"10.18653/v1/W18-3704","DOIUrl":"https://doi.org/10.18653/v1/W18-3704","url":null,"abstract":"This paper investigates what differentiates effective tutorial sessions from less effective sessions. Towards this end, we characterize and explore human tutors’ actions in tutorial dialogue sessions by mapping the tutor-tutee interactions, which are streams of dialogue utterances, into streams of actions, based on the language-as-action theory. Next, we use human expert judgment measures, evidence of learning (EL) and evidence of soundness (ES), to identify effective and ineffective sessions. We perform sub-sequence pattern mining to identify sub-sequences of dialogue modes that discriminate good sessions from bad sessions. We finally use the results of sub-sequence analysis method to generate a tutorial Markov process for effective tutorial sessions.","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":"130551990","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: 3
Assessment of an Index for Measuring Pronunciation Difficulty 语音难度指标的评价
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3717
Katsunori Kotani, T. Yoshimi
{"title":"Assessment of an Index for Measuring Pronunciation Difficulty","authors":"Katsunori Kotani, T. Yoshimi","doi":"10.18653/v1/W18-3717","DOIUrl":"https://doi.org/10.18653/v1/W18-3717","url":null,"abstract":"This study assesses an index for measur-ing the pronunciation difficulty of sen-tences (henceforth, pronounceability) based on the normalized edit distance from a reference sentence to a transcrip-tion of learners’ pronunciation. Pro-nounceability should be examined when language teachers use a computer-assisted language learning system for pronunciation learning to maintain the motivation of learners. However, unlike the evaluation of learners’ pronunciation performance, previous research did not focus on pronounceability not only for English but also for Asian languages. This study found that the normalized edit distance was reliable but not valid. The lack of validity appeared to be because of an English test used for determining the proficiency of learners.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"42 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":"123165733","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
Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement 基于统计和先验知识驱动特征的汉语语法错误诊断
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3707
Ruiji Fu, Zhengqi Pei, Jiefu Gong, Wei Song, Dechuan Teng, Wanxiang Che, Shijin Wang, Guoping Hu, Ting Liu
{"title":"Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement","authors":"Ruiji Fu, Zhengqi Pei, Jiefu Gong, Wei Song, Dechuan Teng, Wanxiang Che, Shijin Wang, Guoping Hu, Ting Liu","doi":"10.18653/v1/W18-3707","DOIUrl":"https://doi.org/10.18653/v1/W18-3707","url":null,"abstract":"This paper describes our system at NLPTEA-2018 Task #1: Chinese Grammatical Error Diagnosis. Grammatical Error Diagnosis is one of the most challenging NLP tasks, which is to locate grammar errors and tell error types. Our system is built on the model of bidirectional Long Short-Term Memory with a conditional random field layer (BiLSTM-CRF) but integrates with several new features. First, richer features are considered in the BiLSTM-CRF model; second, a probabilistic ensemble approach is adopted; third, Template Matcher are used during a post-processing to bring in human knowledge. In official evaluation, our system obtains the highest F1 scores at identifying error types and locating error positions, the second highest F1 score at sentence level error detection. We also recommend error corrections for specific error types and achieve the best F1 performance among all participants.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"395 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886100","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: 25
Chinese Grammatical Error Diagnosis Based on Policy Gradient LSTM Model 基于策略梯度LSTM模型的汉语语法错误诊断
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3710
Changliang Li, Ji Qi
{"title":"Chinese Grammatical Error Diagnosis Based on Policy Gradient LSTM Model","authors":"Changliang Li, Ji Qi","doi":"10.18653/v1/W18-3710","DOIUrl":"https://doi.org/10.18653/v1/W18-3710","url":null,"abstract":"Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA2018 workshop held during ACL2018. The goal of this task is to diagnose Chinese sentences containing four kinds of grammatical errors through the model and find out the sentence errors. Chinese grammatical error diagnosis system is a very important tool, which can help Chinese learners automatically diagnose grammatical errors in many scenarios. However, due to the limitations of the Chinese language’s own characteristics and datasets, the traditional model faces the problem of extreme imbalances in the positive and negative samples and the disappearance of gradients. In this paper, we propose a sequence labeling method based on the Policy Gradient LSTM model and apply it to this task to solve the above problems. The results show that our model can achieve higher precision scores in the case of lower False positive rate (FPR) and it is convenient to optimize the model on-line.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"6 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":"114997694","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
Syntactic and Lexical Approaches to Reading Comprehension 阅读理解的句法和词汇方法
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3702
Henry Lin
{"title":"Syntactic and Lexical Approaches to Reading Comprehension","authors":"Henry Lin","doi":"10.18653/v1/W18-3702","DOIUrl":"https://doi.org/10.18653/v1/W18-3702","url":null,"abstract":"Among the challenges of teaching reading comprehension in K – 12 are identifying the portions of a text that are difficult for a student, comprehending major critical ideas, and understanding context-dependent polysemous words. We present a simple, unsupervised but robust and accurate syntactic method for achieving the first objective and a modified hierarchical lexical method for the second objective. Focusing on pinpointing troublesome sentences instead of the overall readability and on concepts central to a reading, we believe these methods will greatly facilitate efforts to help students improve reading skills","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":"115024314","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
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