15 citations · 22 across the 4 of their papers we have counts for
9 papers · 1 filter
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables
Ali Araabi, Vlad Niculae, Christof Monz
Despite the tremendous success of Neural Machine Translation (NMT), its performance on low-resource language pairs still remains subpar, partly due to the limited ability to handle…
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens
David Stap, Vlad Niculae, Christof Monz
We argue that translation quality alone is not a sufficient metric for measuring knowledge transfer in multilingual neural machine translation. To support this claim, we introduce…
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins
Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously…
Adaptively Sparse Transformers
Gonçalo M. Correia, Vlad Niculae, André F. T. Martins
Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed a…
Sparse Sequence-to-Sequence Models
Ben Peters, Vlad Niculae, André F. T. Martins
Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a softmax transformation in both their attention mechanism and output layer, leading to dense alig…
Towards Dynamic Computation Graphs via Sparse Latent Structure
Vlad Niculae, André F. T. Martins, Claire Cardie
Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent st…