2019 International Conference on Asian Language Processing (IALP)最新文献_第9页

Character Decomposition for Japanese-Chinese Character-Level Neural Machine Translation 日中字符级神经机器翻译的字符分解

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037677

Jinyi Zhang, Tadahiro Matsumoto

引用次数: 2

How to Answer Comparison Questions 如何回答比较问题

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037729

Hongxuan Tang, Yu Hong, Xin Chen, Kaili Wu, Min Zhang

{"title":"How to Answer Comparison Questions","authors":"Hongxuan Tang, Yu Hong, Xin Chen, Kaili Wu, Min Zhang","doi":"10.1109/IALP48816.2019.9037729","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037729","url":null,"abstract":"“Which city has the larger population, Tokyo or New York?”. To answer the question, in general, we necessarily obtain the prior knowledge about the populations of both cities, and accordingly determine the answer by numeric comparison. Using Machine Reading Comprehension (MRC) to answer such a question has become a popular research topic, which is referred to as a task of Comparison Question Answering (CQA). In this paper, we propose a novel neural CQA model which is trained to answer comparison question. The model is designed as a sophisticated neural network which performs inference in a step-by-step pipeline, including the steps of attentive entity detection (e.g., “city”), alignment of comparable attributes (e.g., “population” of the target “cities”), contrast calculation (larger or smaller), as well as binary classification of positive and negative answers. The experimentation on HotpotQA illustrates that the proposed method achieves an average F1 score of 63.09%, outperforming the baseline with about 10% F1 scores. In addition, it performs better than a series of competitive models, including DecompRC, BERT.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758401","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

An Enhancement of Malay Social Media Text Normalization for Lexicon-Based Sentiment Analysis 马来语社交媒体文本规范化在基于词汇的情感分析中的改进

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037700

Muhammad Fakhrur Razi Abu Bakar, N. Idris, Liyana Shuib

引用次数: 6

Acoustic Scene Classification Using Deep Convolutional Neural Network via Transfer Learning 基于迁移学习的深度卷积神经网络声学场景分类

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037692

Min Ye, Hong Zhong, Xiao Song, Shilei Huang, Gang Cheng

引用次数: 2

Sinhala and Tamil Speech Intent Identification From English Phoneme Based ASR 基于英语音素ASR的僧伽罗语和泰米尔语语音意图识别

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037702

Yohan Karunanayake, Uthayasanker Thayasivam, Surangika Ranathunga

{"title":"Sinhala and Tamil Speech Intent Identification From English Phoneme Based ASR","authors":"Yohan Karunanayake, Uthayasanker Thayasivam, Surangika Ranathunga","doi":"10.1109/IALP48816.2019.9037702","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037702","url":null,"abstract":"Today we can find many use cases for content-based speech classification. These include speech topic identification and spoken command recognition. Automatic Speech Recognition (ASR) sits underneath all of these applications to convert speech into textual format. However, creating an ASR system for a language is a resource-consuming task. Even though there are more than 6000 languages, all of these speech-related applications are limited to the most well-known languages such as English, because of the availability of data. There is some past research that looked into classifying speech while addressing the data scarcity. However, all of these methods have their own limitations. In this paper, we present an English language phoneme based speech intent classification methodology for Sinhala and Tamil languages. We use a pre-trained English ASR model to generate phoneme probability features and use them to identify intents of utterances expressed in Sinhala and Tamil, for which a rather small speech dataset is available. The experiment results show that the proposed method can have more than 80% accuracy for a 0.5-hour limited speech dataset in both languages.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121422077","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

Design and Implementation of Burmese Speech Synthesis System Based on HMM-DNN 基于HMM-DNN的缅甸语语音合成系统的设计与实现

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037731

Mengyuan Liu, Jian Yang

引用次数: 1

Learning Deep Matching-Aware Network for Text Recommendation using Clickthrough Data 使用点击数据学习文本推荐的深度匹配感知网络

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037682

Haonan Liu, Nankai Lin, Zitao Chen, Ke Li, Sheng-yi Jiang

{"title":"Learning Deep Matching-Aware Network for Text Recommendation using Clickthrough Data","authors":"Haonan Liu, Nankai Lin, Zitao Chen, Ke Li, Sheng-yi Jiang","doi":"10.1109/IALP48816.2019.9037682","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037682","url":null,"abstract":"With the trend of information globalization, the volume of text information is exploding, which results in the information overload problem. Text recommendation system has shown to be a valuable tool to help users in such situations of information overload. In general, most researchers define text recommendation as a static problem, ignoring sequential information. In this paper, we propose a text recommendation framework with matching-aware interest extractor and dynamic interest extractor. We apply the Attention-based Long Short-Term Memory Network (LSTM) to model a user’ s dynamic interest. Besides, we model a user’ s static interest with the idea of semantic matching. We integrate dynamic interest and static interest of users’ and decide whether to recommend a text. We also propose a reasonable method to construct a text recommendation dataset with clickthrough data from CCIR 2018 shared task Personal Recommendation. We test our model and other baseline models on the dataset. The experiment shows our model outperforms all the baseline models and a state-of-the-art model, and the Fl-score of our model reaches 0.76.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126418842","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

combination of Semantic Relatedness with Supervised Method for Word Sense Disambiguation 语义关联与监督相结合的词义消歧方法

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI: 10.1109/IALP48816.2019.9037717

Qiaoli Zhou, Yuguang Meng

引用次数: 1

Tibetan word segmentation method based on CNN-BiLSTM-CRF model 基于CNN-BiLSTM-CRF模型的藏文分词方法

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2018-11-01 DOI: 10.1109/IALP48816.2019.9037661

Lili Wang, Hongwu Yang, Xiaotian Xing, Yajing Yan

{"title":"Tibetan word segmentation method based on CNN-BiLSTM-CRF model","authors":"Lili Wang, Hongwu Yang, Xiaotian Xing, Yajing Yan","doi":"10.1109/IALP48816.2019.9037661","DOIUrl":"https://doi.org/10.1109/IALP48816.2019.9037661","url":null,"abstract":"We propose a Tibetan word segmentation method based on CNN-BiLSTM-CRF model that merely uses the characters of sentence as the input so that the method does not need large-scale corpus resources and manual features for training. Firstly, we use convolution neural network to train character vectors. Then the character vectors are searched through the character lookup table to form a matrix C by stacking searched results. Then the convolution operation between the matrix C and multiple filter matrices is carried out to obtain the character-level features of each Tibetan word by maximizing the pooling. We input the character vector into the BiLSTM-CRF model, which is suitable for Tibetan word segmentation through the highway network, for getting a Tibetan word segmentation model that is optimized by using the character vector and CRF model. For Tibetan language with rich morphology, fewer parameters and faster training time make this model better than BiLSTM-CRF model in the performance of character level. The experimental results show that character input is sufficient for language modeling. The robustness of Tibetan word segmentation is improved by the model that can achieves 95.17% of the F value.","PeriodicalId":208066,"journal":{"name":"2019 International Conference on Asian Language Processing (IALP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994165","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