[PDF] Top 20 Domain Control for Neural Machine Translation
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Domain Control for Neural Machine Translation
... Josep Crego, Jungi Kim, Guillaume Klein, Anabel Re- bollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johan- son, Ardas Khalsa, Raoum ... See full document
7
Domain Adaptation of Neural Machine Translation by Lexicon Induction
... learning domain dis- crimination and translation (Britz et ...and translation (Gulcehre et al., 2015; Domhan and Hieber, 2017), and domain control by adding tags and word fea- tures ... See full document
13
Instance Weighting for Neural Machine Translation Domain Adaptation
... For Neural Machine Translation (NMT) domain adaptation, the sentence selection can also be used (Chen et ...and domain control (Kobus et ...word-level domain features to ... See full document
7
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
... In this paper, we propose a novel do- main adaptation method named “mixed fine tuning” for neural machine transla- tion (NMT). We combine two existing ap- proaches namely fine tuning and multi do- main NMT. ... See full document
7
Multi Domain Neural Machine Translation through Unsupervised Adaptation
... ral Machine Translation (NMT) under the following three conditions posed by real- world application ...without domain in- ...each domain and appli- cable only by violating two of our afore- ... See full document
11
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
... As a final preprocessing step, we train Byte Pair Encoding (BPE) segmentation models (Sennrich et al., 2016) on the out-of-domain training corpus. We train separate BPE models for each language, each with a ... See full document
9
Unsupervised Domain Adaptation for Neural Machine Translation with Domain Aware Feature Embeddings
... While neural machine translation (NMT) sys- tems have proven to be effective in scenarios where large amounts of in-domain data are avail- able (Gehring et ...test domain does not match ... See full document
6
Cost Weighting for Neural Machine Translation Domain Adaptation
... Starting from random parameters for both mod- els, we alternate between optimizing the weighted NMT objective in Equation 7, and the classifier’s cross-entropy objective. Training the two concur- rently allows the ... See full document
7
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... get domain by comparing the distances of their sentence embeddings to the embeddings of the generic ...the domain of climate change and politics, ... See full document
10
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... Elastic Weight Consolidation (EWC) (Kirk- patrick et al., 2017) is a method for training neu- ral networks to learn a new task without for- getting previously learned tasks. We extend EWC to continued training in NMT ... See full document
7
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
... Josep Maria Crego, Jungi Kim, Guillaume Klein, An- abel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, ... See full document
9
NAVER Machine Translation System for WAT 2015
... Neural machine translation (NMT) is a new ap- proach to machine translation that has shown promising results compared to the existing ap- proaches such as phrase-based statistical ... See full document
5
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... We did our study in the domain of news articles and professions. However, human corpora has a broad spectrum of categories, as an instance: in- dustrial, medical, legal that may rise other biases particular to ... See full document
8
Neural vs Phrase Based Machine Translation in a Multi Domain Scenario
... Neural machine translation systems have recently outperformed their conventional statistical coun- terparts in the translation tasks in several domains such as news (Sennrich et ...target ... See full document
5
Curriculum Learning for Domain Adaptation in Neural Machine Translation
... score domain relevance has a different impact on the TED domain (top plots) and on the patent domain (bottom ...patent domain, which is more distant from Paracrawl, CDS significantly ... See full document
13
Effective Domain Mixing for Neural Machine Translation
... Neural Machine Translation (NMT) models are often trained on hetero- geneous mixtures of domains, from news to parliamentary proceedings, each with unique distributions and lan- ...ing domain ... See full document
9
Sentence Embedding for Neural Machine Translation Domain Adaptation
... For model adaptation, several PBSMT models, such as language models, translation models and reordering models, individually corresponding to each corpus, are trained. These models are then combined to achieve the ... See full document
7
Domain Differential Adaptation for Neural Machine Translation
... out-of- domain data and then select data that are similar to in-domain text based on the resulting scores, a paradigm adapted by Duh et ...in domain adapta- tion ... See full document
11
Domain Adaptive Inference for Neural Machine Translation
... data domain to match with the best adapted model, let alone optimal weights for an ensemble on that do- ...out domain labelling using our adaptive decod- ing schemes with unadapted models trained only on ... See full document
7
Iterative Dual Domain Adaptation for Neural Machine Translation
... the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of- domain translation knowledge to in-domain NMT ...the ... See full document
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