Papers by Miguel Rios
Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
| Approach: | They exploit translation as a distributional context and embed words as posterior probability densities, rather than point estimates, which allows them to compare words in context using a measure of overlap between distributions. |
| Outcome: | The proposed model performs on a range of lexical semantics tasks and achieves competitive results on benchmarks including natural language inference, paraphrasing, and text similarity. |
Latent Variable Model for Multi-modal Translation (P19-1)
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| Challenge: | Libovick and Helcl (2017) show improvements due to imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable and training on additional target-language image descriptions. |
| Approach: | They propose to model interaction between visual and textual features for multi-modal neural machine translation (MMT) using a latent variable model. |
| Outcome: | The proposed model improves over baselines including a multi-task learning approach and a conditional variational auto-encoder approach. |