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Found 8 papers, 0 papers with code
Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets
Recent research in the field of multimodal machine translation (MMT) has indicated that the visual modality is either dispensable or offers only marginal advantages.
Multimodal Neural Machine Translation with Search Engine Based Image Retrieval
However, most of these conclusions are drawn from the analysis of experimental results based on a limited set of bilingual sentence-image pairs, such as Multi30K.
Collecting Indicators of Compromise from Unstructured Text of Cybersecurity Articles using Neural-Based Sequence Labelling
Indicators of Compromise (IOCs) are artifacts observed on a network or in an operating system that can be utilized to indicate a computer intrusion and detect cyber-attacks in an early stage.
Automatic Identification of Indicators of Compromise using Neural-Based Sequence Labelling
In this paper, we propose using a neural-based sequence labelling model to identify IOCs automatically from reports on cybersecurity without expert knowledge of cybersecurity.
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.
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.
