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Found 7 papers, 6 papers with code
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation
Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT).
Finetuning a Kalaallisut-English machine translation system using web-crawled data
We compile a corpus of over 93, 000 Kalaallisut sentences and over 140, 000 Danish sentences, then use cross-lingual sentence embeddings and approximate nearest-neighbors search in an attempt to mine near-translations from these corpora.
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
The heterogeneity in ML models comes from multi-sensor perceiving and multi-task learning, i. e., multi-modality multi-task (MMMT), resulting in diverse deep neural network (DNN) layers and computation patterns.
A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space
We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.
Sentiment-based Candidate Selection for NMT
The explosion of user-generated content (UGC)--e. g. social media posts, comments, and reviews--has motivated the development of NLP applications tailored to these types of informal texts.
Majority Voting with Bidirectional Pre-translation For Bitext Retrieval
Obtaining high-quality parallel corpora is of paramount importance for training NMT systems.
Learning Deep Graph Representations via Convolutional Neural Networks
The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood.
