Pivot-Based Hybrid Machine Translation to Support Multilingual Communication

Rizki²,

et al. 2019

ITJRD

Self Cite

Bahasa Inggris adalah salah satu bahasa yang digunakan sebagai alat komunikasi universal, karenanya tanpa kemampuan bahasa Inggris seorang akan mengalami kesulitan berkomunikasi secara baik dan benar pada ruang lingkup internasional. Penelitian ini mengembangkan aplikasi mesin penerjemah berbasis augmented reality yang dapat memberikan edukasi kepada siswa dengan media yang berbeda agar meningkatkan minat siswa dalam belajar bahasa Inggris. Aplikasi ini menggunakan library vuforia sdk yang mampu menampilkan karakter 3 dimensi dengan teknik markerless dalam bentuk augmented reality. Hasil akhir dari penelitian ini berupa aplikasi yang dapat digunakan pada smartphone dengan sistem operasi android, berdasar hasil pengujian terhadap aplikasi didapat kesimpulan bahwa aplikasi ini dapat menampilkan karakter 3 dimensi pada cahaya yang redup dengan intensitas cahaya 28 lux pada jarak 10cm-60 cm dan sudut penglihatan 10°-90°, setelah dilakukan peninjauan terhadap aplikasi 99% koresponden menyatakan aplikasi ini baik, maka aplikasi ini dapat membantu siswa mempelajari kembali bahasa Inggris selain disekolah.

Section: Mesin Penerjemahunclassified

Section: Pivot-based Hybrid Machine Translationunclassified

Mesin Penerjemah Interaktif Dengan Animasi 3D Berbasis Augmented Reality

Rizki²,

et al. 2019

ITJRD

Self Cite

“…Machine-readable bilingual dictionaries are important language resources which are often utilized as language services [12] for various purpose such as supporting intercultural communication and collaboration [11,15,22]. Unfortunately, low-resource languages lack such resources.…”

Section: Introductionmentioning

confidence: 99%

Plan Optimization to Bilingual Dictionary Induction for Low-resource Language Families

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

Murakami

Ishida

2021

Self Cite

Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still difficult to determine the execution order of the constraint-based approach to reducing the total cost. Plan optimization is crucial in composing the order of bilingual dictionaries creation with the consideration of the methods and their costs. We formalize the plan optimization for creating bilingual dictionaries by utilizing Markov Decision Process (MDP) with the goal to get a more accurate estimation of the most feasible optimal plan with the least total cost before fully implementing the constraint-based bilingual lexicon induction. We model a prior beta distribution of bilingual lexicon induction precision with language similarity and polysemy of the topology as and parameters. It is further used to model cost function and state transition probability. We estimated the cost of all investment plans as a baseline for evaluating the proposed MDP-based approach with total cost as an evaluation metric. After utilizing the posterior beta distribution in the first batch of experiments to construct the prior beta distribution in the second batch of experiments, the result shows 61.5% of cost reduction compared to the estimated all investment plans and 39.4% of cost reduction compared to the estimated MDP optimal plan. The MDP-based proposal outperformed the baseline on the total cost.

Lecture Notes in Networks and Systems

“…Unfortunately, low-resource languages like Indonesian lack such resources. Some prior studies focused on enriching low-resource languages [6,7,8,9,10,11,12,13]. The rapid growth of online textual data creates an urgent need for powerful text mining techniques [1].…”

Section: Introductionmentioning

confidence: 99%

Towards a Sentiment Analyser for Low-resource Languages

Indriani¹,

Nasution²,

Monika³

et al. 2020

Self Cite

Twitter is one of the top influenced social media which has a million number of active users. It is commonly used for microblogging that allows users to share messages, ideas, thoughts and many more. Thus, millions interaction such as short messages or tweets are flowing around among the twitter users discussing various topics that has been happening worldwide. This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time. We chose a hashtag #kpujangancurang that was the trending topic during the Indonesia presidential election in 2019. We use the hashtag to obtain a set of data from Twitter to analyse and investigate further the positive or the negative sentiment of the users from their tweets. This research utilizes rapid miner tool to generate the twitter data and comparing Naive Bayes, K-Nearest Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to classify the sentiment of the twitter data. There are overall 200 labeled data in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron classification outperformed the other two methods on 11 experiments with different size of training-testing data split. The two classifiers are potential to be used in creating sentiment analyzer for low-resource languages with small corpus.