research
∙
11/28/2022
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative m...
share
research
∙
07/18/2022
MAD for Robust Reinforcement Learning in Machine Translation
We introduce a new distributed policy gradient algorithm and show that i...
research
∙
03/01/2022
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale
Transformer language models that are trained on vast amounts of data hav...
research
∙
02/23/2022
Enabling arbitrary translation objectives with Adaptive Tree Search
We introduce an adaptive tree search algorithm, that can find high-scori...
research
∙
06/09/2021
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
We present an end-to-end differentiable training method for retrieval-au...
research
∙
06/07/2021
Diverse Pretrained Context Encodings Improve Document Translation
We propose a new architecture for adapting a sentence-level sequence-to-...
research
∙
06/04/2021
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis–Hastings
While recent work has shown that scores from models trained by the ubiqu...
research
∙
05/27/2020
Syntactic Structure Distillation Pretraining For Bidirectional Encoders
Textual representation learners trained on large amounts of data have ac...
research
∙
05/07/2020
Learning to Segment Actions from Observation and Narration
We apply a generative segmental model of task structure, guided by narra...
research
∙
05/04/2020
A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing
We propose a deep and interpretable probabilistic generative model to an...
research
∙
01/29/2020
Learning Robust and Multilingual Speech Representations
Unsupervised speech representation learning has shown remarkable success...
research
∙
01/22/2020
Transition-Based Dependency Parsing using Perceptron Learner
Syntactic parsing using dependency structures has become a standard tech...
research
∙
10/01/2019
Putting Machine Translation in Context with the Noisy Channel Model
We show that Bayes' rule provides a compelling mechanism for controlling...
research
∙
09/20/2019
A Critical Analysis of Biased Parsers in Unsupervised Parsing
A series of recent papers has used a parsing algorithm due to Shen et al...
research
∙
09/03/2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their emp...
research
∙
08/29/2019
Shallow Syntax in Deep Water
Shallow syntax provides an approximation of phrase-syntactic structure o...
research
∙
06/24/2019
Compound Probabilistic Context-Free Grammars for Grammar Induction
We study a formalization of the grammar induction problem that models se...
research
∙
06/14/2019
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Prior work has shown that, on small amounts of training data, syntactic ...
research
∙
04/15/2019
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search
Globally normalized neural sequence models are considered superior to th...
research
∙
04/07/2019
Unsupervised Recurrent Neural Network Grammars
Recurrent neural network grammars (RNNG) are generative models of langua...
research
∙
01/31/2019
Learning and Evaluating General Linguistic Intelligence
We define general linguistic intelligence as the ability to reuse previo...
research
∙
11/26/2018
Sentence Encoding with Tree-constrained Relation Networks
The meaning of a sentence is a function of the relations that hold betwe...
research
∙
11/23/2018
Unsupervised Word Discovery with Segmental Neural Language Models
We propose a segmental neural language model that combines the represent...
research
∙
08/30/2018
Syntactic Scaffolds for Semantic Structures
We introduce the syntactic scaffold, an approach to incorporating syntac...
research
∙
08/01/2018
Neural Arithmetic Logic Units
Neural networks can learn to represent and manipulate numerical informat...
research
∙
06/11/2018
Finding Syntax in Human Encephalography with Beam Search
Recurrent neural network grammars (RNNGs) are generative models of (tree...
research
∙
06/04/2018
Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
research
∙
05/30/2018
Unsupervised Text Style Transfer using Language Models as Discriminators
Binary classifiers are often employed as discriminators in GAN-based uns...
research
∙
05/23/2018
Pushing the bounds of dropout
We show that dropout training is best understood as performing MAP estim...
research
∙
03/27/2018
Fast Parametric Learning with Activation Memorization
Neural networks trained with backpropagation often struggle to identify ...
research
∙
03/08/2018
Learning Deep Generative Models of Graphs
Graphs are fundamental data structures which concisely capture the relat...
research
∙
01/31/2018
Paraphrase-Supervised Models of Compositionality
Compositional vector space models of meaning promise new solutions to st...
research
∙
12/19/2017
The NarrativeQA Reading Comprehension Challenge
Reading comprehension (RC)---in contrast to information retrieval---requ...
research
∙
08/01/2017
End-to-End Neural Segmental Models for Speech Recognition
Segmental models are an alternative to frame-based models for sequence p...
research
∙
08/01/2017
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
Beam search is a desirable choice of test-time decoding algorithm for ne...
research
∙
07/18/2017
On the State of the Art of Evaluation in Neural Language Models
Ongoing innovations in recurrent neural network architectures have provi...
research
∙
06/29/2017
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
We present a new, efficient frame-semantic parser that labels semantic a...
research
∙
06/08/2017
Dynamic Integration of Background Knowledge in Neural NLU Systems
Common-sense or background knowledge is required to understand natural l...
research
∙
05/22/2017
On-the-fly Operation Batching in Dynamic Computation Graphs
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offe...
research
∙
05/11/2017
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Solving algebraic word problems requires executing a series of arithmeti...
research
∙
05/08/2017
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Type-level word embeddings use the same set of parameters to represent a...
research
∙
04/23/2017
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling
Fixed-vocabulary language models fail to account for one of the most cha...
research
∙
04/23/2017
Differentiable Scheduled Sampling for Credit Assignment
We demonstrate that a continuous relaxation of the argmax operation can ...
research
∙
03/06/2017
Generative and Discriminative Text Classification with Recurrent Neural Networks
We empirically characterize the performance of discriminative and genera...
research
∙
02/21/2017
Multitask Learning with CTC and Segmental CRF for Speech Recognition
Segmental conditional random fields (SCRFs) and connectionist temporal c...
research
∙
01/15/2017
DyNet: The Dynamic Neural Network Toolkit
We describe DyNet, a toolkit for implementing neural network models base...
research
∙
11/28/2016
Learning to Compose Words into Sentences with Reinforcement Learning
We use reinforcement learning to learn tree-structured neural networks f...
research
∙
11/17/2016
What Do Recurrent Neural Network Grammars Learn About Syntax?
Recurrent neural network grammars (RNNG) are a recently proposed probabi...
research
∙
11/08/2016
The Neural Noisy Channel
We formulate sequence to sequence transduction as a noisy channel decodi...
research
∙
11/05/2016