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20162022
most citedA Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation

31 citations · 96 across the 14 of their papers we have counts for

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29 papers · 1 filter

cs.CL20221 cited

Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

Kelly Marchisio, Ali Saad-Eldin, Kevin Duh +2

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual inf…

cs.CL2021

An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces

Kelly Marchisio, Youngser Park, Ali Saad-Eldin +4

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that…

cs.CL2021

Self-Guided Curriculum Learning for Neural Machine Translation

Lei Zhou, Liang Ding, Kevin Duh +3

In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i.e. the synthetic labels predicted by the model should be as close t…

cs.CL2020

Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder

Hirofumi Inaguma, Yosuke Higuchi, Kevin Duh +2

Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems. End-to-end (E2E) models based on the encoder-decoder architecture are mo…

cs.CL2020

Very Deep Transformers for Neural Machine Translation

Xiaodong Liu, Kevin Duh, Liyuan Liu +1

We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we…

cs.CL2020

ESPnet-ST: All-in-One Speech Translation Toolkit

Hirofumi Inaguma, Shun Kiyono, Kevin Duh +4

We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech…