decoder model
Japanese Text Normalization with Encoder Decoder Model
... The contributions of this research can be summarized by citing two points. First, the proposed data augmentation methods can provide stable training of the encoder-decoder model. Second, it can improve the ...
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Generating a Training Corpus for OCR Post Correction Using Encoder Decoder Model
... Recently, Neural Network Language Models have proven to be extremely effective in complex NLP tasks. For spelling errors correction, sys- tems either include auto-encoders to detect near- est neighbor matches of spelling ...
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Arabic machine transliteration using an attention based encoder decoder model
... the decoder attention mechanism leads to a significant ...the decoder attention mechanism was incorporated. The PSMT model and the Bi- Att-seq2seq models gave the best results of ...
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Joint Entity Extraction and Assertion Detection for Clinical Text
... two decoder model consisting of decoders which use the shared encoder representation to jointly predict entities and negation attribute (Figure ...This model mitigates the issues associated with ...
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Noisy Uyghur Text Normalization
... In this work, we propose two models for normaliz- ing Uyghur UULA texts. The noisy channel model views the problem as a spell-checking problem, while the neural encoder-decoder model views it as a ...
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A Mixed Hierarchical Attention Based Encoder Decoder Approach for Standard Table Summarization
... Structured data summarization involves gen- eration of natural language summaries from structured input data. In this work, we con- sider summarizing structured data occurring in the form of tables as they are prevalent ...
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Generative Encoder Decoder Models for Task Oriented Spoken Dialog Systems with Chatting Capability
... the decoder to generate the system utterances word by word, and the dialog policy of the proposed model can be fine tuned us- ing reinforcement learning (Su et ...end-to-end model that maps an ...
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Abstractive Text Image Summarization Using Multi Modal Attentional Hierarchical RNN
... Encoder-Decoder model in text ...summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, ...
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A Working Memory Model for Task oriented Dialog Response Generation
... encoder-decoder model, where decoder is the Working Memory (WM) which could in- teract with two long-term memories (the episodic memory memorizing dialog history and semantic memory storing KB ...
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Variational Neural Machine Translation
... variational model to learn this conditional distribution for neu- ral machine translation: a variational encoder- decoder model that can be trained ...encoder-decoder model that ...
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Large Scale Transfer Learning for Natural Language Generation
... input model, the pretrained language model is duplicated in an encoder-decoder architecture ...single-input model, natu- ral separators, spatial-separator tokens or context- type embeddings ...
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Using Target side Monolingual Data for Neural Machine Translation through Multi task Learning
... baseline model consists of a 1- layer bi-directional LSTM encoder with an embed- ding size of 512 and a hidden size of ...LSTM decoder with 1024 hidden units uses an attention network with 256 hidden ...The ...
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An Efficient A* Stack Decoder Algorithm for Continuous Speech Recognition with a Stochastic Language Model
... An Efficient A* Stack Decoder Algorithm for Continuous Speech Recognition with a Stochastic Language Model A n E f f i c i e n t A * S t a c k D e c o d e r A l g o r i t h m for C o n t i n u o u s S[.] ...
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Optimal M BCJR Turbo Decoding: The Z MAP Algorithm
... The results are unexpected. The simulation shows that the performance of the Turbo decoder Z-MAP are very close to those of Full-MAP (note that the proposed algorithm works with the reduced complexity 12.3 only). ...
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MT/IE: Cross lingual Open Information Extraction with Neural Sequence to Sequence Models
... Cross-lingual information extraction is the task of distilling facts from foreign lan- guage (e.g. Chinese text) into represen- tations in another language that is pre- ferred by the user (e.g. English tuples). ...
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Incorporating Source Syntax into Transformer Based Neural Machine Translation
... In this paper, we proposed two methods for in- corporating source-side syntactic annotations into a Transformer-based neural machine translation system. The first, multi-task, used a shared en- coder and decoder ...
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Efficient Solutions for Word Reordering in German English Phrase Based Statistical Machine Translation
... Feature weights are optimized for each exper- iment using the procedure described above (four averaged MERT runs). Statistical significance is computed for each experiment against the pre- vious one (i. e. previous row), ...
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Design, Optimization and Synthesis of Efficient Reversible Logic Binary Decoder
... Enoch Hwang et all [4] showed that although power reduction techniques can be applied at every level of design abstraction, most automated power reduction techniques apply to the lower levels of design abstraction, such ...
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A Fast Decoder for Joint Word Segmentation and POS Tagging Using a Single Discriminative Model
... the model is initialized as all zeros before training, and used to decode training ...the decoder prediction is compared with the training ...the decoder prediction, just as the perceptron algo- ...
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Speeding up Context based Sentence Representation Learning with Non autoregressive Convolutional Decoding
... As presented in Table 2, the encoders that only sum over pretrained word vectors perform better overall than those with RNNs on unsupervised eval- uation tasks, including STS14. In recent proposed log-bilinear models, ...
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