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Joint learning of frequency and word embeddings for multilingual readability assessment 多语言可读性评估中频率与词嵌入的联合学习
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3714
Dieu-Thu Le, Cam-Tu Nguyen, Xiaoliang Wang
{"title":"Joint learning of frequency and word embeddings for multilingual readability assessment","authors":"Dieu-Thu Le, Cam-Tu Nguyen, Xiaoliang Wang","doi":"10.18653/v1/W18-3714","DOIUrl":"https://doi.org/10.18653/v1/W18-3714","url":null,"abstract":"This paper describes two models that employ word frequency embeddings to deal with the problem of readability assessment in multiple languages. The task is to determine the difficulty level of a given document, i.e., how hard it is for a reader to fully comprehend the text. The proposed models show how frequency information can be integrated to improve the readability assessment. The experimental results testing on both English and Chinese datasets show that the proposed models improve the results notably when comparing to those using only traditional word embeddings.","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":"134211182","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
CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task 中校三组汉语语法错误诊断系统在NLPTEA-2018 CGED共享任务中的报告
NLP-TEA@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-3729
Shih-Hung Wu, Jun-Wei Wang, Liang-Pu Chen, Ping-Che Yang
{"title":"CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task","authors":"Shih-Hung Wu, Jun-Wei Wang, Liang-Pu Chen, Ping-Che Yang","doi":"10.18653/v1/W18-3729","DOIUrl":"https://doi.org/10.18653/v1/W18-3729","url":null,"abstract":"This paper reports how we build a Chinese Grammatical Error Diagnosis system in the NLPTEA-2018 CGED shared task. In 2018, we sent three runs with three different approaches. The first one is a pattern-based approach by frequent error pattern matching. The second one is a sequential labelling approach by conditional random fields (CRF). The third one is a rewriting approach by sequence to sequence (seq2seq) model. The three approaches have different properties that aim to optimize different performance metrics and the formal run results show the differences as we expected.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"45 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":"116954410","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: 9
Feature Optimization for Predicting Readability of Arabic L1 and L2 预测阿拉伯语L1和L2可读性的特征优化
NLP-TEA@ACL Pub Date : 2018-06-29 DOI: 10.18653/v1/W18-3703
Hind Saddiki, Nizar Habash, V. Cavalli-Sforza, M. Al-Khalil
{"title":"Feature Optimization for Predicting Readability of Arabic L1 and L2","authors":"Hind Saddiki, Nizar Habash, V. Cavalli-Sforza, M. Al-Khalil","doi":"10.18653/v1/W18-3703","DOIUrl":"https://doi.org/10.18653/v1/W18-3703","url":null,"abstract":"Advances in automatic readability assessment can impact the way people consume information in a number of domains. Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment. In this paper, we present the largest and most in-depth computational readability study for Arabic to date. We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks. Our best L1 readability accuracy result is 94.8% (75% error reduction from a commonly used baseline). The comparable results for L2 are 72.4% (45% error reduction). We also demonstrate the added value of leveraging L1 features for L2 readability prediction.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129935845","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
Learning to Automatically Generate Fill-In-The-Blank Quizzes 学习自动生成填空测验
NLP-TEA@ACL Pub Date : 2018-06-12 DOI: 10.18653/v1/W18-3722
Edison Marrese-Taylor, Ai Nakajima, Y. Matsuo, Y. Ono
{"title":"Learning to Automatically Generate Fill-In-The-Blank Quizzes","authors":"Edison Marrese-Taylor, Ai Nakajima, Y. Matsuo, Y. Ono","doi":"10.18653/v1/W18-3722","DOIUrl":"https://doi.org/10.18653/v1/W18-3722","url":null,"abstract":"In this paper we formalize the problem automatic fill-in-the-blank question generation using two standard NLP machine learning schemes, proposing concrete deep learning models for each. We present an empirical study based on data obtained from a language learning platform showing that both of our proposed settings offer promising results.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407830","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: 15
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model 基于CRF和LSTM-CRF模型的汉语语法错误诊断
NLP-TEA@ACL Pub Date : 1900-01-01 DOI: 10.18653/v1/W18-3724
Yujie Zhou, Yinan Shao, Yong Zhou
{"title":"Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model","authors":"Yujie Zhou, Yinan Shao, Yong Zhou","doi":"10.18653/v1/W18-3724","DOIUrl":"https://doi.org/10.18653/v1/W18-3724","url":null,"abstract":"When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122254442","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: 4
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task cmc - bdrc解决NLP-TEA-2018汉语语法错误诊断任务
NLP-TEA@ACL Pub Date : 1900-01-01 DOI: 10.18653/v1/W18-3726
Yongwei Zhang, Q. Hu, Fang Liu, Yueguo Gu
{"title":"CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task","authors":"Yongwei Zhang, Q. Hu, Fang Liu, Yueguo Gu","doi":"10.18653/v1/W18-3726","DOIUrl":"https://doi.org/10.18653/v1/W18-3726","url":null,"abstract":"Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual & Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121519024","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
Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model 基于BiLSTM-CRF模型的汉语语法错误同步检测
NLP-TEA@ACL Pub Date : 1900-01-01 DOI: 10.18653/v1/W18-3727
Yajun Liu, Hongying Zan, Mengjie Zhong, Hongchao Ma
{"title":"Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model","authors":"Yajun Liu, Hongying Zan, Mengjie Zhong, Hongchao Ma","doi":"10.18653/v1/W18-3727","DOIUrl":"https://doi.org/10.18653/v1/W18-3727","url":null,"abstract":"In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132100529","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: 6
Detecting Grammatical Errors in the NTOU CGED System by Identifying Frequent Subsentences 通过识别频繁子句来检测nou CGED系统中的语法错误
NLP-TEA@ACL Pub Date : 1900-01-01 DOI: 10.18653/v1/W18-3730
Chuan-Jie Lin, Shao-Heng Chen
{"title":"Detecting Grammatical Errors in the NTOU CGED System by Identifying Frequent Subsentences","authors":"Chuan-Jie Lin, Shao-Heng Chen","doi":"10.18653/v1/W18-3730","DOIUrl":"https://doi.org/10.18653/v1/W18-3730","url":null,"abstract":"The main goal of Chinese grammatical error diagnosis task is to detect word er-rors in the sentences written by Chinese-learning students. Our previous system would generate error-corrected sentences as candidates and their sentence likeli-hood were measured based on a large scale Chinese n-gram dataset. This year we further tried to identify long frequent-ly-seen subsentences and label them as correct in order to avoid propose too many error candidates. Two new methods for suggesting missing and selection er-rors were also tested.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131898544","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
Selecting NLP Techniques to Evaluate Learning Design Objectives in Collaborative Multi-perspective Elaboration Activities 选择NLP技术评估协同多视角精化活动中的学习设计目标
NLP-TEA@ACL Pub Date : 1900-01-01 DOI: 10.18653/v1/W18-3712
Aneesha Bakharia
{"title":"Selecting NLP Techniques to Evaluate Learning Design Objectives in Collaborative Multi-perspective Elaboration Activities","authors":"Aneesha Bakharia","doi":"10.18653/v1/W18-3712","DOIUrl":"https://doi.org/10.18653/v1/W18-3712","url":null,"abstract":"PerspectivesX is a multi-perspective elaboration tool designed to encourage learner submission and curation across a range of collaborative learning activities. In this paper, it is shown that the learning design objectives of collaborative learning activities can be evaluated using NLP techniques, but that careful analysis of learner impact and pedagogical intent are required in order to select appropriate techniques. In particular, this paper focuses on the NLP techniques required to deliver an instructor dashboard, personalized learner feedback and content recommendation within multi-perspective elaboration activities. Key NLP techniques considered for inclusion include summarization, topic modeling, paraphrase detection and diversified content recommendation.","PeriodicalId":321264,"journal":{"name":"NLP-TEA@ACL","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134409074","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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