[PDF] Top 20 Major-minor long short-term memory for word-level language model
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Major-minor long short-term memory for word-level language model
... LSTM language model called Major-Minor LSTMs (MMLSTMs), which employs two LSTMs of different scales to generate the output features ...original language model without impairing ... See full document
15
Long Short-Term Memory with Dynamic Skip Connections
... years, long short-term memory (LSTM) has been successfully used to model sequential data of variable ...capturing long-term ...to model the dependency relationship ... See full document
8
Cached Long Short Term Memory Neural Networks for Document Level Sentiment Classification
... on long texts by extracting semantic in- formation hierarchically (Tang et ...a long-range one (Mc- Donald et ...reading word by word and are capable to model the intrinsic relations ... See full document
10
A Long Short Term Memory Model for Answer Sentence Selection in Question Answering
... bidirectional Long-Short Term Memory (BLSTM) net- work to sequentially read words from question and answer sentences, and then outputs their relevance ...relevance model and keywords ... See full document
6
Modelling Radiological Language with Bidirectional Long Short Term Memory Networks
... with long input sequences (Bengio et ...a long short- term memory (LSTM) cell, which allows for a con- stant error flow along the input sequence (Hochre- iter and Schmidhuber, ...ter ... See full document
11
Predicting Polarities of Tweets by Composing Word Embeddings with Long Short Term Memory
... Distributed word vectors help in various NLP tasks when using in neural models (Collobert et ...sentence level information also improves performance of sentiment analy- sis (Yessenalina and Cardie, ... See full document
11
Long Short Term Memory Networks for Machine Reading
... the word embeddings were set to 150 for all ...5-gram language model (KN5) which generally serves as a non-neural baseline for the language modeling ...connect memory cells of ... See full document
11
Long Short Term Memory Neural Networks for Chinese Word Segmentation
... nese language processing. In recent years, Chi- nese word segmentation (CWS) has undergone great ...gard word segmentation task as a sequence label- ing problem (Xue, 2003; Peng et ... See full document
10
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories
... different language mod- els which utilize machine learning algorithms have been crafted and tested for accuracy and ...and long short-term memory (LSTM) neural networks (Sundermeyer et ... See full document
5
Classification of Medication Related Tweets Using Stacked Bidirectional LSTMs with Context Aware Attention
... (MSA) model of (Baziotis et al., 2017), a word- level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with at- tention, to classify medication-related ... See full document
5
Gated Word Character Recurrent Language Model
... guage model (RNN-LM) with long short- term memory (LSTM) units that utilizes both character-level and word-level ...Our model has a gate that adaptively ... See full document
6
Convolutional Neural Network Language Models
... feed-forward language model (FFLM) is almost identical to the original model proposed by Bengio et ...The model is illustrated in Figure 1 and works as follows. First, each word in the ... See full document
10
Advanced recurrent network-based hybrid acoustic models for low resource speech recognition
... to model temporal ...years, long short-term memory RNNs (LSTM RNNs) have been proposed to solve this problem and have achieved excellent ... See full document
15
THE EFFECT OF HYPERGLYCEMIA O HIPPOCAMPUS EUROAL DESITY I FEMALE RATS
... The hippocampus is necessary for normal cognitive function, especially for processing recognition memory and transferring short-term memory items into long term storage. ... See full document
10
Parallelizable Stack Long Short Term Memory
... Another toolkit that supports automatic mini- batching is Matchbox 6 , which operates by analyz- ing the single-instance model definition and deter- ministically convert the operations into their mini- batched ... See full document
6
Recurrent Memory Networks for Language Modeling
... natural language process- ing, a common assumption is that LSTMs are able to capture certain linguistic ...compression model hurts the per- formance of overall ... See full document
11
NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT BiLSTM Attention Model
... Table 4 shows the results of our testing models. In addition to the BioBERT-Base model, the other models achieved promising accuracy. As expected, our ensemble strategy has the better performance. Our ensemble ... See full document
5
Predicting the Amount of Digestive Enzymes Medicine Usage with LSTM
... Predictions of medicine use can be useful for stakeholders in hospitals or other health institutions to make decisions about managing medicine resources. For future research, it is still needed to compare to another LSTM ... See full document
5
The Lasting Effects of Language Acquisition: Testing Cognitive Abilities after L2 Attrition
... Phonological short-term memory, working memory and foreign language performance in intensive language learning.. Learner-Based Factors in L2 Reading Comprehension and Processing Grammati[r] ... See full document
9
Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide
... However, short life time and poor reliability and mobility of organic materials and liquid electrolytes are a major concern for realising high performance electronic ...and short-term to ... See full document
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