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Found 11 papers, 1 papers with code
End-to-end Image Captioning Exploits Distributional Similarity in Multimodal Space
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn {`}distributional similarity{'} in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space.
Sheffield Submissions for WMT18 Multimodal Translation Shared Task
For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
Prepositional Phrase Attachment over Word Embedding Products
We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task).
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.
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation
Out-of-vocabulary words account for a large proportion of errors in machine translation systems, especially when the system is used on a different domain than the one where it was trained.
Mapping Unseen Words to Task-Trained Embedding Spaces
We consider the supervised training setting in which we learn task-specific word embeddings.
Structured Prediction with Output Embeddings for Semantic Image Annotation
We address the task of annotating images with semantic tuples.
Tailoring Word Embeddings for Bilexical Predictions: An Experimental Comparison
We investigate the problem of inducing word embeddings that are tailored for a particular bilexical relation.
