b'Chris Dyer'

research

∙ 11/28/2022

Continuous diffusion for categorical data

Diffusion models have quickly become the go-to paradigm for generative m...

0 Sander Dieleman, et al. ∙

research

∙ 07/18/2022

MAD for Robust Reinforcement Learning in Machine Translation

We introduce a new distributed policy gradient algorithm and show that i...

0 Domenic Donato, et al. ∙

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...

0 Laurent Sartran, et al. ∙

research

∙ 02/23/2022

Enabling arbitrary translation objectives with Adaptive Tree Search

We introduce an adaptive tree search algorithm, that can find high-scori...

0 Wang Ling, et al. ∙

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...

0 Devendra Singh Sachan, et al. ∙

research

∙ 06/07/2021

Diverse Pretrained Context Encodings Improve Document Translation

We propose a new architecture for adapting a sentence-level sequence-to-...

0 Domenic Donato, et al. ∙

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...

0 Kartik Goyal, et al. ∙

research

∙ 05/27/2020

Syntactic Structure Distillation Pretraining For Bidirectional Encoders

Textual representation learners trained on large amounts of data have ac...

0 Adhiguna Kuncoro, et al. ∙

research

∙ 05/07/2020

Learning to Segment Actions from Observation and Narration

We apply a generative segmental model of task structure, guided by narra...

4 Daniel Fried, et al. ∙

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...

0 Kartik Goyal, et al. ∙

research

∙ 01/29/2020

Learning Robust and Multilingual Speech Representations

Unsupervised speech representation learning has shown remarkable success...

0 Kazuya Kawakami, et al. ∙

research

∙ 01/22/2020

Transition-Based Dependency Parsing using Perceptron Learner

Syntactic parsing using dependency structures has become a standard tech...

12 Rahul Radhakrishnan Iyer, et al. ∙

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...

0 Lei Yu, et al. ∙

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...

0 Chris Dyer, 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...

1 Po-Sen Huang, et al. ∙

research

∙ 08/29/2019

Shallow Syntax in Deep Water

Shallow syntax provides an approximation of phrase-syntactic structure o...

0 Swabha Swayamdipta, et al. ∙

research

∙ 06/24/2019

Compound Probabilistic Context-Free Grammars for Grammar Induction

We study a formalization of the grammar induction problem that models se...

0 Yoon Kim, et al. ∙

research

∙ 06/14/2019

Scalable Syntax-Aware Language Models Using Knowledge Distillation

Prior work has shown that, on small amounts of training data, syntactic ...

0 Adhiguna Kuncoro, et al. ∙

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...

0 Kartik Goyal, et al. ∙

research

∙ 04/07/2019

Unsupervised Recurrent Neural Network Grammars

Recurrent neural network grammars (RNNG) are generative models of langua...

0 Yoon Kim, et al. ∙

research

∙ 01/31/2019

Learning and Evaluating General Linguistic Intelligence

We define general linguistic intelligence as the ability to reuse previo...

8 Dani Yogatama, et al. ∙

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...

0 Lei Yu, et al. ∙

research

∙ 11/23/2018

Unsupervised Word Discovery with Segmental Neural Language Models

We propose a segmental neural language model that combines the represent...

0 Kazuya Kawakami, et al. ∙

research

∙ 08/30/2018

Syntactic Scaffolds for Semantic Structures

We introduce the syntactic scaffold, an approach to incorporating syntac...

0 Swabha Swayamdipta, et al. ∙

research

∙ 08/01/2018

Neural Arithmetic Logic Units

Neural networks can learn to represent and manipulate numerical informat...

6 Andrew Trask, et al. ∙

research

∙ 06/11/2018

Finding Syntax in Human Encephalography with Beam Search

Recurrent neural network grammars (RNNGs) are generative models of (tree...

0 John Hale, et al. ∙

research

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

0 Peter W. Battaglia, et al. ∙

research

∙ 05/30/2018

Unsupervised Text Style Transfer using Language Models as Discriminators

Binary classifiers are often employed as discriminators in GAN-based uns...

0 Zichao Yang, et al. ∙

research

∙ 05/23/2018

Pushing the bounds of dropout

We show that dropout training is best understood as performing MAP estim...

0 Gábor Melis, et al. ∙

research

∙ 03/27/2018

Fast Parametric Learning with Activation Memorization

Neural networks trained with backpropagation often struggle to identify ...

0 Jack W Rae, et al. ∙

research

∙ 03/08/2018

Learning Deep Generative Models of Graphs

Graphs are fundamental data structures which concisely capture the relat...

0 Yujia Li, et al. ∙

research

∙ 01/31/2018

Paraphrase-Supervised Models of Compositionality

Compositional vector space models of meaning promise new solutions to st...

0 Avneesh Saluja, et al. ∙

research

∙ 12/19/2017

The NarrativeQA Reading Comprehension Challenge

Reading comprehension (RC)---in contrast to information retrieval---requ...

0 Tomáš Kočiský, et al. ∙

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...

0 Hao Tang, et al. ∙

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...

0 Kartik Goyal, et al. ∙

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...

0 Gábor Melis, et al. ∙

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...

0 Swabha Swayamdipta, et al. ∙

research

∙ 06/08/2017

Dynamic Integration of Background Knowledge in Neural NLU Systems

Common-sense or background knowledge is required to understand natural l...

0 Dirk Weissenborn, et al. ∙

research

∙ 05/22/2017

On-the-fly Operation Batching in Dynamic Computation Graphs

Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offe...

0 Graham Neubig, et al. ∙

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...

0 Wang Ling, et al. ∙

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...

0 Pradeep Dasigi, et al. ∙

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...

0 Kazuya Kawakami, et al. ∙

research

∙ 04/23/2017

Differentiable Scheduled Sampling for Credit Assignment

We demonstrate that a continuous relaxation of the argmax operation can ...

0 Kartik Goyal, et al. ∙

research

∙ 03/06/2017

Generative and Discriminative Text Classification with Recurrent Neural Networks

We empirically characterize the performance of discriminative and genera...

0 Dani Yogatama, et al. ∙

research

∙ 02/21/2017

Multitask Learning with CTC and Segmental CRF for Speech Recognition

Segmental conditional random fields (SCRFs) and connectionist temporal c...

0 Liang Lu, et al. ∙

research

∙ 01/15/2017

DyNet: The Dynamic Neural Network Toolkit

We describe DyNet, a toolkit for implementing neural network models base...

0 Graham Neubig, et al. ∙

research

∙ 11/28/2016

Learning to Compose Words into Sentences with Reinforcement Learning

We use reinforcement learning to learn tree-structured neural networks f...

0 Dani Yogatama, et al. ∙

research

∙ 11/17/2016

What Do Recurrent Neural Network Grammars Learn About Syntax?

Recurrent neural network grammars (RNNG) are a recently proposed probabi...

0 Adhiguna Kuncoro, et al. ∙

research

∙ 11/08/2016

The Neural Noisy Channel

We formulate sequence to sequence transduction as a noisy channel decodi...

0 Lei Yu, et al. ∙

research

∙ 11/05/2016

Reference-Aware Language Models

We propose a general class of language models that treat reference as an...

0 Zichao Yang, et al. ∙

Chris Dyer

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