[PDF] Top 20 Classical Structured Prediction Losses for Sequence to Sequence Learning
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Classical Structured Prediction Losses for Sequence to Sequence Learning
... for structured prediction tasks in NLP (Gimpel and Smith, 2010) and apply them to a neural sequence to sequence model (Gehring et ...the sequence- level, a margin loss as well as ... See full document
10
Observational sequence learning
... observational sequence learning using a standard two action test incorporating both location and directional ...a sequence the individual sequence elements must be available within a rats ... See full document
189
Sentence-Wise Smooth Regularization for Sequence to Sequence Learning
... in sequence to sequence learning, sentences with non-smooth probabilities are usually of low ...more sequence generation tasks, such as image captioning and question ...probability ... See full document
8
The involvement of the fronto-parietal brain network in oculomotor sequence learning using fMRI.
... motor learning involves decomposing complete actions into a series of predictive individual components that form the ...short-term learning, by using a novel sequence learning paradigm that is ... See full document
41
Non sentential Question Resolution using Sequence to Sequence Learning
... as sequence to sequence learning, maps a variable length input sequence to a variable length output ...as sequence to sequence ...input sequence by concatenating NSU ... See full document
10
Unsupervised Pretraining for Sequence to Sequence Learning
... We presented a novel unsupervised pretraining method to improve sequence to sequence learning. The method can aid in both generalization and op- timization. Our scheme involves pretraining two ... See full document
9
Bandit Structured Prediction for Neural Sequence to Sequence Learning
... lift structured pre- diction under bandit feedback from linear models to non-linear sequence-to-sequence learning us- ing recurrent neural networks with ... See full document
11
Attention Strategies for Multi Source Sequence to Sequence Learning
... multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its use- fulness in tasks that incorporate multiple source languages or ...source sequence, flat and ... See full document
7
Self Regulated Interactive Sequence to Sequence Learning
... tive sequence-to-sequence learning, with a self- regulation module at its core that learns which type of feedback to query from a human ...models learning from a single feedback type and ... See full document
13
Neural Sequence to sequence Learning of Internal Word Structure
... character-level sequence-to-sequence transformation with a language model over canonical seg- ...and classical statistical machine transla- tion systems trained with and without cor- pus counts, we ... See full document
11
Sequence to Sequence Learning as Beam Search Optimization
... and sequence-labeling ...global sequence scores. This structured approach avoids classical biases as- sociated with local training and unifies the training loss with the test-time usage, while ... See full document
11
Exploring Sequence to Sequence Learning in Aspect Term Extraction
... a sequence labeling problem and extract more use- ful features surrounding a ...However, sequence labeling meth- ods are not good at grasping the overall meaning of the whole sentence because they cannot ... See full document
10
Morphological Inflection Generation Using Character Sequence to Sequence Learning
... input sequence, which is then used along with the decoder hidden layer to make a prediction (Bah- danau et ...character sequence as inputs to the de- ...character sequence in a vector e by ... See full document
10
Incorporating Copying Mechanism in Sequence to Sequence Learning
... In this paper, we explore another mechanism important to the human language communication, called the “copying mechanism”. Basically, it refers to the mechanism that locates a certain seg- ment of the input sentence and ... See full document
10
Sequence to Sequence Learning for Event Prediction
... Mart´ın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Cor- rado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, ... See full document
6
Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
... It was discussed in Section 2, that sequence prediction and sequence recognition are not always the same. Here, the idea is tested in practice. Fortunately, in the case of seismic data, there are ... See full document
6
Sequence-to-sequence modeling for graph representation learning
... LSTM sequence-to-sequence learning framework of (Sutskever et ...input sequence into a vector and another LSTM to generate the output sequence from that ...same sequence as both ... See full document
26
Tutorial: De mystifying Neural MT
... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document
84
Error-Correcting Neural Sequence Prediction
... are required. They too propose to use Gumbel-Softmax trick but for the purposes of learning the discrete codes. The per- formance was maintaned for sentiment analysis and machine translation with 94% and 98% ... See full document
9
Structured prediction models for RNN based sequence labeling in clinical text
... current Neural Networks), on the other hand, have been shown to be extremely good at identifying pat- terns from noisy text data, but they still predict each word label in isolation and not as a part of a se- quence. In ... See full document
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