Search Results for author:
Found 18 papers, 7 papers with code
Interactive Assignments for Teaching Structured Neural NLP
We present a set of assignments for a graduate-level NLP course.
Towards End-to-End In-Image Neural Machine Translation
In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language.
Semantic Scaffolds for Pseudocode-to-Code Generation
We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program.
Imitation Attacks and Defenses for Black-box Machine Translation Systems
To mitigate these vulnerabilities, we propose a defense that modifies translation outputs in order to misdirect the optimization of imitation models.
Insertion-Deletion Transformer
We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation.
An Empirical Study of Generation Order for Machine Translation
In this work, we present an empirical study of generation order for machine translation.
KERMIT: Generative Insertion-Based Modeling for Sequences
During training, one can feed KERMIT paired data $(x, y)$ to learn the joint distribution $p(x, y)$, and optionally mix in unpaired data $x$ or $y$ to refine the marginals $p(x)$ or $p(y)$.
Ranked #39 on
Machine Translation
on WMT2014 English-German
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations.
Blockwise Parallel Decoding for Deep Autoregressive Models
Deep autoregressive sequence-to-sequence models have demonstrated impressive performance across a wide variety of tasks in recent years.
What's Going On in Neural Constituency Parsers? An Analysis
A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity.
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
In several recently proposed stochastic optimization methods (e. g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients.
Stochastic Cubic Regularization for Fast Nonconvex Optimization
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006].
Effective Inference for Generative Neural Parsing
Generative neural models have recently achieved state-of-the-art results for constituency parsing.
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results.
Ranked #14 on
Constituency Parsing
on Penn Treebank
Kernel Feature Selection via Conditional Covariance Minimization
We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response.
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks.
A Minimal Span-Based Neural Constituency Parser
In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans.
Abstract Syntax Networks for Code Generation and Semantic Parsing
Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs.
Ranked #3 on
Semantic Parsing
on ATIS
