[PDF] Top 20 Neural Text Generation from Rich Semantic Representations
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Neural Text Generation from Rich Semantic Representations
... that neural sequence-to-sequence models can be used to generate high quality nat- ural language text from Minimal Recursion Se- mantics representations, in contrast to both ex- isting ... See full document
8
Automated Chess Commentator Powered by Neural Chess Engine
... collected from FICS Database 1 and unsupervised self-play (Silver et ...the generation models. It is able to get board representations, predict reasonable move distributions, and give continuous ... See full document
10
Machine Comprehension using Rich Semantic Representations
... taken from Richardson et ...constructed from the question and the can- didate answer to the ...taken from Smith et ...taken from Sachan et al. (2015), use neural net- works (LTSMs and ... See full document
7
Semantic Parsing for Text to 3D Scene Generation
... input text to identify a subset of relevant objects matching X = {O s , C s } where O s is the set of object descriptions and C s is the set of object ...are semantic representations of objects, ... See full document
5
Back Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations
... Language Generation (NLG) from semantic representations for non- English ...as semantic repre- sentation and language, ...and Neural Machine Translation) are evaluated on two ... See full document
10
Improving Language Generation from Feature Rich Tree Structured Data with Relational Graph Convolutional Encoders
... deep neural networks could lead to gradient vanishing problem, thus we add residual connections (He et ...hidden representations of nodes into one hidden vector h C for the whole ... See full document
6
Neural Text Generation in Stories Using Entity Representations as Context
... Of course, the use of automatic annotations from a coreference system will introduce noise and risks “confusing” the entity-aware models. The benefit is that we were able to train on a much larger corpus than any ... See full document
11
Enhancing AMR to Text Generation with Dual Graph Representations
... Semantic Adequacy We also evaluate the se- mantic adequacy of our model (how well does the generated output match the input?) by compar- ing the number of added and missing tokens that occur in the generated ... See full document
12
Computing Semantic Text Similarity Using Rich Features
... ate semantic similarity between short English sen- ...the semantic similarity score between sentence ...good generation ability on novel datasets which means this technique could be well general- ... See full document
9
Neural Sentiment Classification with User and Product Attention
... local text in- formation and ignore the global user pref- erence and product ...suffer from high model complexity and only consider word- level preference rather than semantic ...cal neural ... See full document
10
Data to text Generation with Entity Modeling
... and neural network ...entity representations for language ...entity representations fixed during ...entity representations in machine reading. Entity representations are scored against ... See full document
13
A Neural Network Component for Knowledge-Based Semantic Representations of Text
... a neural network, which can be explained as high-level concepts that are au- tomatically discovered during the learning pro- cess (Bengio, ...the neural network is able to build high-level con- cepts ... See full document
8
Personalized Neural Embeddings for Collaborative Filtering with Text
... suffer from the data sparsity ...unstructured text such as article abstracts and product ...Personalized Neural Embedding (PNE) framework to exploit both interaction- s and words ...and ... See full document
7
Predicting sentential semantic compatibility for aggregation in text to text generation
... extracted from a given sentence could be combined back) and establish a gold-standard clustering to compare the results of the task with, we conducted a user ...originated from the same original sentence, ... See full document
10
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings
... keyphrases from a document, that is both simple and only requires the current document itself, rather than an entire corpus that this document may be linked ...in text representation learning (Le et ... See full document
9
Unlocking the Potential of Recommender Systems: A Framework to Achieve Multiple Domain Recommendations
... the Semantic Logger and is parsed into RDF ...priate representations: they have been taken from proposed standards at the W3C or other standard making bodies, or have been selected due to current ... See full document
52
Comparative Study of Text Summarization Methods
... Luhn[4] proposed that the most frequent words represent the most important concept of the text. His idea was to give the score to each sentence based on number of occurrences of the words and then choose the ... See full document
5
Text Generation using Neural Models
... samples from a given (simple) prior distribution to synthetic data that appear realistic, while the discriminator aims to distinguish the fake data from real ...of text renders the model ... See full document
5
Machine Comprehension by Text to Text Neural Question Generation
... Question answering (QA) One obvious mea- sure of a question’s quality is whether it can be answered correctly given the context document D. We therefore feed model-generated questions into a pretrained question-answering ... See full document
11
Contextualized Word Representations for Reading Comprehension
... LM from J´ozefowicz et ...states from the top-most stacked LSTM layer of the LMs, whereas we also consider reading the hidden states of the bottom one, which substan- tially improves ...utilizing ... See full document
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