519 citations · 4.1k across the 233 of their papers we have counts for
19 papers · 1 filter
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Hao Zhang, Shizhen Xu, Graham Neubig +4
Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences…
Convolutional Neural Networks for Medical Diagnosis from Admission Notes
Christy Li, Dimitris Konomis, Graham Neubig +3
Develop an automatic diagnostic system which only uses textual admission information from Electronic Health Records (EHRs) and assist clinicians with a timely…
Improving Neural Machine Translation through Phrase-based Forced Decoding
Jingyi Zhang, Masao Utiyama, Eiichro Sumita +2
Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine th…
Transcribing Against Time
Matthias Sperber, Graham Neubig, Jan Niehues +2
We investigate the problem of manually correcting errors from an automatic speech transcript in a cost-sensitive fashion. This is done by specifying a fixed time budget, and then a…
Handling Homographs in Neural Machine Translation
Frederick Liu, Han Lu, Graham Neubig
Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translati…
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
Kartik Goyal, Graham Neubig, Chris Dyer +1
Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, ty…
Learning Language Representations for Typology Prediction
Chaitanya Malaviya, Graham Neubig, Patrick Littell
One central mystery of neural NLP is what neural models "know" about their subject matter. When a neural machine translation system learns to translate from one language to another…
CharManteau: Character Embedding Models For Portmanteau Creation
Varun Gangal, Harsh Jhamtani, Graham Neubig +2
Portmanteaus are a word formation phenomenon where two words are combined to form a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of p…
Stronger Baselines for Trustable Results in Neural Machine Translation
Michael Denkowski, Graham Neubig
Interest in neural machine translation has grown rapidly as its effectiveness has been demonstrated across language and data scenarios. New research regularly introduces architectu…
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Makoto Morishita, Yusuke Oda, Graham Neubig +3
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes. During the mini-batched training process, it is necessary to pad shorter sent…
On-the-fly Operation Batching in Dynamic Computation Graphs
Graham Neubig, Yoav Goldberg, Chris Dyer
Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer more flexibility for implementing models that cope with data of varying dimensions and structure, relative…
Learning Character-level Compositionality with Visual Features
Frederick Liu, Han Lu, Chieh Lo +1
Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing system…
Controllable Invariance through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du +2
Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine…
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML
Xuezhe Ma, Pengcheng Yin, Jingzhou Liu +2
Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a…
Neural Machine Translation via Binary Code Prediction
Yusuke Oda, Philip Arthur, Graham Neubig +2
In this paper, we propose a new method for calculating the output layer in neural machine translation systems. The method is based on predicting a binary code for each word and can…
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Chunting Zhou, Graham Neubig
Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels. In this paper we propose multi-sp…
Neural Lattice-to-Sequence Models for Uncertain Inputs
Matthias Sperber, Graham Neubig, Jan Niehues +1
The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream mod…
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Graham Neubig
This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…