English-Indonesian Neural Machine Translation for Spoken Language Domains

2019-07-01 · ACL 2019 7 · Meisyarah Dwiastuti

In this work, we conduct a study on Neural Machine Translation (NMT) for English-Indonesian (EN-ID) and Indonesian-English (ID-EN). We focus on spoken language domains, namely colloquial and speech languages. We build NMT systems using the Transformer model for both translation directions and implement domain adaptation, in which we train our pre-trained NMT systems on speech language (in-domain) data. Moreover, we conduct an evaluation on how the domain-adaptation method in our EN-ID system can result in more formal translation outputs.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Domain Adaptation Machine TranslationNMTTranslation

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Position-Wise Feed-Forward Layer 설명 없음

Residual Connection 설명 없음

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…

ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…

IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT

2016-12-01 · WS 2016 12 · S. Singh, hya, Anoop Kunchukuttan, Pushpak Bhattacharyya

This paper describes the IIT Bombay{'}s submission as a part of the shared task in WAT 2016 for English{--}Indonesian language pair. The results reported here are for both the direction of the language pair. Among the va…

Language Modeling Language Modelling Machine Translation Translation

Crossing Language Borders: A Pipeline for Indonesian Manhwa Translation

2025-01-03 · Nithyasri Narasimhan, Sagarika Singh

In this project, we develop a practical and efficient solution for automating the Manhwa translation from Indonesian to English. Our approach combines computer vision, text recognition, and natural language processing te…

Machine Translation Object Detection Optical Character Recognition (OCR)Translation

Handling Indonesian Clitics: A Dataset Comparison for an Indonesian-English Statistical Machine Translation System

2012-11-01 · PACLIC 2012 11 · Septina Dian Larasati

Machine Translation Translation Word Alignment

Benchmarking Multidomain English-Indonesian Machine Translation

2020-05-01 · LREC 2020 5 · Tri Wahyu Guntara, Alham Fikri Aji, Radityo Eko Prasojo

In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large trainin…

Benchmarking Machine Translation NMT Translation

Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator

2022-07-01 · Mukhlis Amien, Feng Chong, Huang Heyan

Indonesian is an agglutinative language since it has a compounding process of word-formation. Therefore, the translation model of this language requires a mechanism that is even lower than the word level, referred to as …

Machine Translation NMT Translation