[PDF] Top 20 Sentence-Wise Smooth Regularization for Sequence to Sequence Learning
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Sentence-Wise Smooth Regularization for Sequence to Sequence Learning
... Label Smoothing (Szegedy et al. 2015; Vaswani et al. 2017) is related to our work in the sense that it makes the true label less confident in training. While applying the cross-entropy loss to a classification task, it ... See full document
8
Observational sequence learning
... of learning which is responsible for the acquisition of this particular feeding behaviour as it is possible that the observed regional differences in food preparation could be attributed entirely to individual ... See full document
189
Bandit Structured Prediction for Neural Sequence to Sequence Learning
... In this paper, we showed how to lift structured pre- diction under bandit feedback from linear models to non-linear sequence-to-sequence learning us- ing recurrent neural networks with attention. We ... See full document
11
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
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
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
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
Neural Sequence to sequence Learning of Internal Word Structure
... It can be seen in Table 2 that cSMT outper- forms cED in the token regime. One possible ex- planation for this outcome is that the inclusion of the word counts helps to learn the character align- ments better. This ... See full document
11
Exploring Sequence to Sequence Learning in Aspect Term Extraction
... a sentence and is usually modeled as a sequence labeling prob- ...However, sequence labeling based meth- ods cannot make full use of the overall mean- ing of the whole sentence and have the ... See full document
10
Incorporating Copying Mechanism in Sequence to Sequence Learning
... in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which cer- tain segments in the input sequence are selectively replicated in the output se- ...Seq2Seq learning and ... See full document
10
Sequence to Sequence Learning as Beam Search Optimization
... shuffled sentence has recently gained some attention as a way to test the (syntactic) capa- bilities of text-generation systems (Zhang and Clark, 2011; Zhang and Clark, 2015; Liu et ...shuffled sentence as ... See full document
11
Classical Structured Prediction Losses for Sequence to Sequence Learning
... Our final experiment evaluates sequence-level training on Gigaword headline summarization. There has been much prior art on this dataset orig- inally introduced by Rush et al. (2015) who ex- periment with a ... See full document
10
Sequence to Sequence Learning for Event Prediction
... Gather ingredients. Turn on oven. Combine ingredients into a bowl. Pour batter in pan. Put pan in oven. Bake for specified time. The task is to predict event description “Put pan in oven” from sentence “Pour ... See full document
6
Morphological Inflection Generation Using Character Sequence to Sequence Learning
... The model we described so far relies entirely on the availability of pairs of root form and inflected word form for learning to generate inflections. Although such supervised models can be used to obtain inflec- ... See full document
10
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
Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task
... that sequence prediction and sequence learning are not the same, in ...a sequence of zeroes and ones, ...the sequence 0,1,0,0,1,0,0,1,0,0,1,0,01,0 (Figure ...a sequence ... See full document
6
Learning to Copy for Automatic Post Editing
... two-source sequence-to-sequence learning problem in which both src and mt play important ...multi-source sequence-to-sequence learning, the lack of the interaction between src ... See full document
11
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
Sentence Level Grammatical Error Identification as Sequence to Sequence Correction
... The recent confluence of data availability and strong sequence-to-sequence learning algorithms has the potential to lead to practical tools for writing sup- port. Grammatical error identification is ... See full document
10
On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning
... abstract sequence A to constitute an overall vector F to represent the whole input sentence W and the two entity mentions of interest ...deep learning models (Nguyen and Grish- man, 2015a; Zhang et ... See full document
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