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Found 15 papers, 1 papers with code
TweetMT: A Parallel Microblog Corpus
We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula.
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation
We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation.
Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity
This is the Lump team participation at SemEval 2017 Task 1 on Semantic Textual Similarity.
Self-Supervised Neural Machine Translation
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations.
UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems
This paper describes the UdS-DFKI submission to the WMT2019 news translation task for Gujarati{--}English (low-resourced pair) and German{--}English (document-level evaluation).
Context-Aware Neural Machine Translation Decoding
This work presents a decoding architecture that fuses the information from a neural translation model and the context semantics enclosed in a semantic space language model based on word embeddings.
Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yor\`ub\'a and Twi
In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing.
Multilingual and Interlingual Semantic Representations for Natural Language Processing: A Brief Introduction
We introduce the Computational Linguistics special issue on Multilingual and Interlingual Semantic Representations for Natural Language Processing.
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.
