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Simple Search
Hits 1 – 11 of 11
1
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
Huang, Po-Yao
;
Patrick, Mandela
;
Hu, Junjie
. - : arXiv, 2021
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2
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ...
Ruder, Sebastian
;
Constant, Noah
;
Botha, Jan
. - : arXiv, 2021
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3
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages ...
Reid, Machel
;
Hu, Junjie
;
Neubig, Graham
. - : arXiv, 2021
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4
DEEP: DEnoising Entity Pre-training for Neural Machine Translation ...
Hu, Junjie
;
Hayashi, Hiroaki
;
Cho, Kyunghyun
. - : arXiv, 2021
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5
Explicit Alignment Objectives for Multilingual Bidirectional Encoders ...
NAACL 2021 2021
;
Firat, Orhan
;
Hu, Junjie
. - : Underline Science Inc., 2021
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6
Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ...
NAACL 2021 2021
;
Hauptmann, Alexander
;
Hu, Junjie
. - : Underline Science Inc., 2021
BASE
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7
Phrase-level Active Learning for Neural Machine Translation ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Hu, Junjie
;
Neubig, Graham
. - : Underline Science Inc., 2021
Abstract:
Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating in-domain data, and gradually fine-tune a pre-trained out-of-domain NMT model on the newly translated data. Existing active learning methods for NMT usually select sentences based on uncertainty scores, but these methods require costly translation of full sentences even when only one or two key phrases within the sentence are informative. To address this limitation, we re-examine previous work from the phrase-based machine translation (PBMT) era that selected not full sentences, but rather individual phrases. However, while incorporating these phrases into PBMT systems was relatively simple, it is less trivial for NMT systems, which need to be trained on full sequences to capture larger structural properties of sentences unique to the new domain. To overcome these hurdles, we propose to select both full sentences and individual ...
Keyword:
Bilingual Lexicon Induction
;
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Machine translation
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/a5he-ja64
https://underline.io/lecture/39477-phrase-level-active-learning-for-neural-machine-translation
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8
Explicit Alignment Objectives for Multilingual Bidirectional Encoders ...
Hu, Junjie
;
Johnson, Melvin
;
Firat, Orhan
. - : arXiv, 2020
BASE
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9
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ...
Hu, Junjie
;
Ruder, Sebastian
;
Siddhant, Aditya
. - : arXiv, 2020
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10
Domain Adaptation of Neural Machine Translation by Lexicon Induction ...
Hu, Junjie
;
Xia, Mengzhou
;
Neubig, Graham
. - : arXiv, 2019
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11
Rapid Adaptation of Neural Machine Translation to New Languages ...
Neubig, Graham
;
Hu, Junjie
. - : arXiv, 2018
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