Search Results for author: Raphael Shu

Found 17 papers, 7 papers with code

Federated Semi-Supervised Learning with Prototypical Networks

1 code implementation27 May 2022 Woojung Kim, Keondo Park, Kihyuk Sohn, Raphael Shu, Hyung-Sin Kim

Compared to a FSSL approach based on weight sharing, the prototype-based inter-client knowledge sharing significantly reduces both communication and computation costs, enabling more frequent knowledge sharing between more clients for better accuracy.

Federated Learning

Reward Optimization for Neural Machine Translation with Learned Metrics

1 code implementation15 Apr 2021 Raphael Shu, Kang Min Yoo, Jung-Woo Ha

Results show that the reward optimization with BLEURT is able to increase the metric scores by a large margin, in contrast to limited gain when training with smoothed BLEU.

Machine Translation NMT +1

GraphPlan: Story Generation by Planning with Event Graph

no code implementations INLG (ACL) 2021 Hong Chen, Raphael Shu, Hiroya Takamura, Hideki Nakayama

In this paper, we focus on planning a sequence of events assisted by event graphs, and use the events to guide the generator.

Story Generation

Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation

1 code implementation EMNLP 2020 Jason Lee, Raphael Shu, Kyunghyun Cho

Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log probability of the target sentence, using only the latent variable as input.

Machine Translation Translation

Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior

1 code implementation20 Aug 2019 Raphael Shu, Jason Lee, Hideki Nakayama, Kyunghyun Cho

By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1. 0 BLEU point on WMT'14 En-De task with 6. 8x speedup.

Machine Translation Translation

Generating Diverse Translations with Sentence Codes

no code implementations ACL 2019 Raphael Shu, Hideki Nakayama, Kyunghyun Cho

In this work, we attempt to obtain diverse translations by using sentence codes to condition the sentence generation.

Machine Translation Translation

Real-time Neural-based Input Method

no code implementations ICLR 2019 Jiali Yao, Raphael Shu, Xinjian Li, Katsutoshi Ohtsuki, Hideki Nakayama

The input method is an essential service on every mobile and desktop devices that provides text suggestions.

Language Modelling

Discrete Structural Planning for Generating Diverse Translations

no code implementations27 Sep 2018 Raphael Shu, Hideki Nakayama

Planning is important for humans when producing complex languages, which is a missing part in current language generation models.

Machine Translation Text Generation +1

Discrete Structural Planning for Neural Machine Translation

no code implementations14 Aug 2018 Raphael Shu, Hideki Nakayama

Structural planning is important for producing long sentences, which is a missing part in current language generation models.

Machine Translation Text Generation +1

Improving Beam Search by Removing Monotonic Constraint for Neural Machine Translation

no code implementations ACL 2018 Raphael Shu, Hideki Nakayama

However, as the algorithm produces hypotheses in a monotonic left-to-right order, a hypothesis can not be revisited once it is discarded.

Language Modelling Machine Translation +1

Single-Queue Decoding for Neural Machine Translation

1 code implementation6 Jul 2017 Raphael Shu, Hideki Nakayama

Neural machine translation models rely on the beam search algorithm for decoding.

Machine Translation Translation

Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation

no code implementations11 Apr 2017 Raphael Shu, Hideki Nakayama

For extended periods of time, sequence generation models rely on beam search algorithm to generate output sequence.

Machine Translation Translation

Residual Stacking of RNNs for Neural Machine Translation

no code implementations WS 2016 Raphael Shu, Akiva Miura

To enhance Neural Machine Translation models, several obvious ways such as enlarging the hidden size of recurrent layers and stacking multiple layers of RNN can be considered.

Machine Translation NMT +2

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