Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation

Wang, Rui; Zhao, Hai; Lu, Bao-Liang; Utiyama, Masao; Sumita, Eiichiro

doi:10.1109/taslp.2015.2425220

Cited by 26 publications

(11 citation statements)

References 35 publications

(86 reference statements)

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“…Hence, the features are expressed in a meaningful way. The n-gram technique is implemented to capture the semantic relationship and calculate the frequency of the feature order [37]. The n-gram model is truncated as k −1 and characterizes the sequence of i features into unigram, bigram, and trigram.…”

Section: Methodsmentioning

confidence: 99%

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Kukkar

Mohana

Nayyar

et al. 2019

Sensors

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The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

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Section: Methodsmentioning

confidence: 99%

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Kukkar

Mohana

Nayyar

et al. 2019

Sensors

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“…Word2vec uses word vector presentation mode based on Distributed representation. Distributed representation is proposed by Hinton in 1986 [28]. Its basic thought is to map each word into a -dimension real vector by training ( is a hyperparameter in the model) and to judge the semantic similarity between them according to the distance between words (such as cosine similarity, Euclidean distance).…”

Section: Word2vecmentioning

confidence: 99%

Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

Dong

Wang

Cai

et al. 2019

Wireless Communications and Mobile Computing

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Crowdsourcing is the perfect show of collective intelligence, and the key of finishing perfectly the crowdsourcing task is to allocate the appropriate task to the appropriate worker. Now the most of crowdsourcing platforms select tasks through tasks search, but it is short of individual recommendation of tasks. Tag-semantic task recommendation model based on deep learning is proposed in the paper. In this paper, the similarity of word vectors is computed, and the semantic tags similar matrix database is established based on the Word2vec deep learning. The task recommending model is established based on semantic tags to achieve the individual recommendation of crowdsourcing tasks. Through computing the similarity of tags, the relevance between task and worker is obtained, which improves the robustness of task recommendation. Through conducting comparison experiments on Tianpeng web dataset, the effectiveness and applicability of the proposed model are verified.

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“…On the other hand, the statistical approach [3]- [6] is datadriven, requiring only a large bilingual corpus. This means that linguists are not required to develop the translation rules.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neural Machine Translation With Weakly Labeled Images

2019

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Machine translation refers to a fully automated process that translates a user's input text into a target language. To improve the accuracy of machine translation, studies usually exploit not only the input text itself but also various background knowledge related to the text, such as visual information or prior knowledge. Herein, in this paper, we propose a multimodal neural machine translation system that uses both texts and their related images to translate Korean image captions into English. The data in the experiment is a set of unlabeled images only containing bilingual captions. To train the system with a supervised learning approach, we propose a weak-labeling method that selects a keyword from an image caption using feature selection methods. The keywords are used to roughly determine an image label. We also introduce an improved feature selection method using sentence clustering to select keywords that reflect the characteristics of the image captions more accurately. We found that our multimodal system achieves an improved performance compared to a text-only neural machine translation system (baseline). Furthermore, the additional images have positive impacts on addressing the issue of under-translation, where some words in a source sentence are falsely translated or not translated at all. INDEX TERMS Human-computer interaction, multi-layer neural network, natural language processing, image classification, multimodal neural machine translation, weak label.

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Bilingual Continuous-Space Language Model Growing for Statistical Machine Translation

Cited by 26 publications

References 35 publications

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting

Recommendation of Crowdsourcing Tasks Based on Word2vec Semantic Tags

Multimodal Neural Machine Translation With Weakly Labeled Images

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