546 citations · 1.2k across the 17 of their papers we have counts for
11 papers · 1 filter
Towards End-to-End In-Image Neural Machine Translation
Elman Mansimov, Mitchell Stern, Mia Chen +3
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 th…
Semantic Scaffolds for Pseudocode-to-Code Generation
Ruiqi Zhong, Mitchell Stern, Dan Klein
We propose a method for program generation based on semantic scaffolds, lightweight structures representing the high-level semantic and syntactic composition of a program. By first…
Imitation Attacks and Defenses for Black-box Machine Translation Systems
Eric Wallace, Mitchell Stern, Dawn Song
Adversaries may look to steal or attack black-box NLP systems, either for financial gain or to exploit model errors. One setting of particular interest is machine translation (MT),…
An Empirical Study of Generation Order for Machine Translation
William Chan, Mitchell Stern, Jamie Kiros +1
In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-re…
KERMIT: Generative Insertion-Based Modeling for Sequences
William Chan, Nikita Kitaev, Kelvin Guu +2
We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., mar…
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros +1
We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which…
What's Going On in Neural Constituency Parsers? An Analysis
David Gaddy, Mitchell Stern, Dan Klein
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 lexico…
Effective Inference for Generative Neural Parsing
Mitchell Stern, Daniel Fried, Dan Klein
Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited t…
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Daniel Fried, Mitchell Stern, Dan Klein
Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficul…
A Minimal Span-Based Neural Constituency Parser
Mitchell Stern, Jacob Andreas, Dan Klein
In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with class…
Abstract Syntax Networks for Code Generation and Semantic Parsing
Maxim Rabinovich, Mitchell Stern, Dan Klein
Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax netwo…