[PDF] Top 20 Bandit Structured Prediction for Neural Sequence to Sequence Learning
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Bandit Structured Prediction for Neural Sequence to Sequence Learning
... dit learning objectives for structured prediction and apply them to various NLP tasks, including machine translation with linear ...inforcement learning to one-state Markov deci- sion ... See full document
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Neural Sequence Learning Models for Word Sense Disambiguation
... that neural sequence learning rep- resents a novel and effective alternative to the tra- ditional way of modeling supervised WSD, en- abling a single all-words model to compete with a pool of word ... See full document
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Neural Sequence to sequence Learning of Internal Word Structure
... Supervised approaches to learning morpholog- ical structure are rather rare. Ruokolainen et al. (2013) apply conditional random fields algorithm, used for different sequence classification tasks, to the ... See full document
11
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
Learning to Summarize Radiology Findings
... and neural baselines on our dataset measured by the standard ROUGE ...with neural sequence-to-sequence learning, and to our knowl- edge our work represents the first attempt in this ... See full document
10
Learning to Stop in Structured Prediction for Neural Machine Translation
... We compare our model with seq2seq, BSO and seq2seq with length reward (Huang et al., 2017) which involves hyper-parameter to solve neural model’s tendency for shorter hypotheses (our pro- posed method does not ... See full document
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Neural ProbabilisticModels for Melody Prediction, Sequence Labelling and Classification
... deep neural network (a feed-forward neural network with more than one hidden layer) was made into reality during this period with the introduction of new meth- ods for pre-training these networks ... See full document
181
Generative Bridging Network for Neural Sequence Prediction
... Sequence prediction has been widely used in tasks where the outputs are sequentially structured and mutually ...based neural sequence prediction models have achieved the ... See full document
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Pervasive Attention: 2D Convolutional Neural Networks for Sequence to Sequence Prediction
... 256 (128 in each direction). The decoder is a sin- gle layer LSTM with similar input size and a hid- den size of 256, the target input embeddings are also used in the pre-softmax projection. For regu- larization, we ... See full document
11
Prediction on DNA Binding Sequence in Deep Learning Approach
... the prediction of DNA- binding proteins only from primary sequences utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory ... See full document
9
Learning Structured Predictors from Bandit Feedback for Interactive NLP
... tured prediction from bandit feedback, with a fo- cus on improving convergence speed and ease of elicitability of ...for bandit pair- wise preference ...preference learning under the criterion ... See full document
11
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
... designed neural net- work models can simulate any Turing machine (Siegelmann and Sontag ...applying neural network models to solve algorithmic tasks such as learning context-sensitive languages (Gers ... See full document
8
Contextualized Non-Local Neural Networks for Sequence Learning
... Table 3: Performance of the proposed models on all datasets compared to typical baselines. × indicates that corresponding models can not work since the sentences are too long to be processed by parser. ∗ denotes ... See full document
8
Error-Correcting Neural Sequence Prediction
... NLM models. We baseline this against both SS and the soft- argmax version of SS, the most related sample-based super- vised learning approach to LMS. Furthermore, we report results on CLMS-ECOC (Curriculum-LMS ... See full document
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Structured prediction models for RNN based sequence labeling in clinical text
... In this work, we explore various methods of struc- tured learning using RNN based feature extractors. We use LSTM as our RNN model. Specifically, we model the CRF pairwise potentials using Neural Networks. ... See full document
10
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
... this learning framework has been com- bined with recurrent neural networks to solve ma- chine translation (Bahdanau et ...2016), neural architecture search (Zoph and Le, 2017), and device place- ment ... See full document
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Structured Prediction via Learning to Search under Bandit Feedback
... for structured contextual ban- dits, BLS, by combining: locally optimal learning to search (to control the structure of exploration) and doubly robust cost estimation (to control the variance of the cost ... See full document
10
Reliability and Learnability of Human Bandit Feedback for Sequence to Sequence Reinforcement Learning
... deep sequence-to-sequence learn- ing (Bahdanau et ...and sequence-to-sequence learning share firstly the existence of a clearly specified reward func- tion, ...automatic ... See full document
12
Sequence based Structured Prediction for Semantic Parsing
... recurrent neural net- work to map a natural language question into a logical form representation of a KB ...are structured objects obeying certain constraints, are enumer- ated by an underlying grammar and ... See full document
10
Neural sequence modelling for learner error prediction
... Most recent work on second language acquisition (SLA) has focused on intermediate-to-advanced learners in assessment settings driven by a series of shared tasks (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., ... See full document
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