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Found 13 papers, 1 papers with code
The RGNLP Machine Translation Systems for WAT 2018
This paper presents the system description of Machine Translation (MT) system(s) for Indic Languages Multilingual Task for the 2018 edition of the WAT Shared Task.
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data.
Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking
A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented.
ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i. e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification.
Understanding the Effect of Textual Adversaries in Multimodal Machine Translation
It is assumed that multimodal machine translation systems are better than text-only systems at translating phrases that have a direct correspondence in the image.
How Human is Machine Translationese? Comparing Human and Machine Translations of Text and Speech
Some translationese features tend to appear in simultaneous interpreting with higher frequency than in human text translation, but the reasons for this are unclear.
Understanding Translationese in Multi-view Embedding Spaces
Recent studies use a combination of lexical and syntactic features to show that footprints of the source language remain visible in translations, to the extent that it is possible to predict the original source language from the translation.
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification
Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts.
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT
We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021).
Tracing Source Language Interference in Translation with Graph-Isomorphism Measures
Previous research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language.
Towards Debiasing Translation Artifacts
Cross-lingual natural language processing relies on translation, either by humans or machines, at different levels, from translating training data to translating test sets.
Translating away Translationese without Parallel Data
We show how we can eliminate the need for parallel validation data by combining the self-supervised loss with an unsupervised loss.
