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Found 6 papers, 0 papers with code
Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017
Long et al.(2017) proposed to select phrases that contain out-of-vocabulary words using the statistical approach of branching entropy.
Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017
Japio participates in patent subtasks (JPC-EJ/JE/CJ/KJ) with phrase-based statistical machine translation (SMT) and neural machine translation (NMT) systems which are trained with its own patent corpora in addition to the subtask corpora provided by organizers of WAT2017.
Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy
Neural machine translation (NMT), a new approach to machine translation, has achieved promising results comparable to those of traditional approaches such as statistical machine translation (SMT).
Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation
We train an NMT system on bilingual data wherein technical terms are replaced with technical term tokens; this allows it to translate most of the source sentences except technical terms.
Translation Using JAPIO Patent Corpora: JAPIO at WAT2016
In scientific paper subtask, our translations are given lower scores than most translations that are produced by translation engines trained with the in-domain corpora.
