Multi-paraphrase Augmentation to Leverage Neural Caption Translation

no code implementations • IWSLT (EMNLP) 2018 • Johanes Effendi, Sakriani Sakti, Katsuhito Sudoh, Satoshi Nakamura

In this paper, we investigate and utilize neural paraphrasing to improve translation quality in neural MT (NMT), which has not yet been much explored.

Machine Translation NMT +1

Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian

no code implementations • loresmt (COLING) 2022 • Alberto Poncelas, Johanes Effendi

The development of machine translation (MT) has been successful in breaking the language barrier of the world’s top 10-20 languages.

Machine Translation Translation

Rakuten’s Participation in WAT 2022: Parallel Dataset Filtering by Leveraging Vocabulary Heterogeneity

no code implementations • WAT 2022 • Alberto Poncelas, Johanes Effendi, Ohnmar Htun, Sunil Yadav, Dongzhe Wang, Saurabh Jain

This paper introduces our neural machine translation system’s participation in the WAT 2022 shared translation task (team ID: sakura).

Machine Translation Translation

RakutenAI-7B: Extending Large Language Models for Japanese

no code implementations • 21 Mar 2024 • Rakuten Group, Aaron Levine, Connie Huang, Chenguang Wang, Eduardo Batista, Ewa Szymanska, Hongyi Ding, Hou Wei Chou, Jean-François Pessiot, Johanes Effendi, Justin Chiu, Kai Torben Ohlhus, Karan Chopra, Keiji Shinzato, Koji Murakami, Lee Xiong, Lei Chen, Maki Kubota, Maksim Tkachenko, Miroku Lee, Naoki Takahashi, Prathyusha Jwalapuram, Ryutaro Tatsushima, Saurabh Jain, Sunil Kumar Yadav, Ting Cai, Wei-Te Chen, Yandi Xia, Yuki Nakayama, Yutaka Higashiyama

We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models.

Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework

no code implementations • 4 Nov 2020 • Johanes Effendi, Andros Tjandra, Sakriani Sakti, Satoshi Nakamura

Previous research has proposed a machine speech chain to enable automatic speech recognition (ASR) and text-to-speech synthesis (TTS) to assist each other in semi-supervised learning and to avoid the need for a large amount of paired speech and text data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6

Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain

no code implementations • 3 Jun 2019 • Johanes Effendi, Andros Tjandra, Sakriani Sakti, Satoshi Nakamura

Previously, a machine speech chain, which is based on sequence-to-sequence deep learning, was proposed to mimic speech perception and production behavior.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6