An exciting outcome of research at the intersection of language and vision is that of zeroshot learning (ZSL). ZSL promises to scale visual recognition by borrowing distributed semantic models learned from linguistic corpora and turning them into visual recognition models. However the popular word-vector DSM embeddings are relatively impoverished in their expressivity as they model each word as a single vector point. In this paper we explore word-distribution embeddings for ZSL. We present a visual-linguistic mapping for ZSL in the case where words and visual categories are both represented by distributions. Experiments show improved results on ZSL benchmarks due to this better exploiting of intra-concept variability in each modality

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Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods for unsupervised bilingual dictionary induction are based on generative adversarial models, and as such suffer from their well known problems of instability and hyperparameter sensitivity. We present a statistical dependency-based approach to bilingual dictionary induction that is unsupervised-no seed dictionary or parallel corpora required; and introduces no adversary-therefore being much easier to train. Our method performs comparably to adversarial alternatives and outperforms prior non-adversarial methods.

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