[PDF] Top 20 Tree-Structured Neural Decoding

... It would likely not be difficult to increase the decoding accuracy even without incorporating specific prior knowledge. For one thing, the parameters have not been tuned. For another, more questions, and/or more ... See full document

... scores do show improvements Figure 1 centre and right.) Figure 2 illustrates the relation between the two cost measures, showing that in most cases the discrete and continuous costs are identical. Linear relaxation fails ... See full document

... Table 4 shows a comparison of the speeds to predict the next word between the word-based decoder and the character-based decoder when generating a sentence by a beam size of 1. The character-based decoder is about 41 ... See full document

... classification. Tree-structured neural networks, which recur- sively compose the representation of smaller text units into larger text spans along the syntactic parse tree, can tactfully ... See full document

... contrary, neural networks are fast compared to ...the neural networks in order to train state-of-the-art systems even in cases data is ...provide neural networks with additional information, such as ... See full document

... from structured semantic representations is a critical step in task-oriented conversational ...a tree-structured MR can improve the model for better discourse-level structuring and sentence-level ... See full document

... Artificial neural networks ...learning neural network architecture, called Evolving Fuzzy Neural Network (EFuNN) ...evolving neural networks is proposed by Seipone and Bullinaria ... See full document

... We first compare each step in our pipeline to the available baselines. For linearization, we test our models with the same tree encoding and different decoding orders (left-to-right (L2R), right-to-left ... See full document

... recurrent neural networks to a variety of NLP tasks (Bah- danau et ...cally structured objects has prompted efforts to develop neural architectures which explicitly ac- count for their ...include ... See full document

... decision tree models. Structured discriminative compression models (McDonald, 2006) are capable of inte- grating rich features and have been proved effec- tive for this ... See full document

... packet tree while using mutual information as a score function to rank the decodability of each ...improved decoding performance versus approaches based on the firing ... See full document

... tion flows from source post to the current node. The idea of this top-down approach is to generate a strengthened feature vector for each post consid- ering its propagation path, where rumor-indicative features are ... See full document

... information structured by constituency tree and dependency tree into neural attention models for the question answering ...of tree structured in- formation such as knowledge ... See full document

... Recently, neural network based depen- dency parsing has attracted much interest, which can effectively alleviate the prob- lems of data sparsity and feature engineer- ing by using the dense ...in neural ... See full document

... To the best of our knowledge, Goldberg et. al. (2012) first define the concept of dynamic oracle and propose an online algorithm for parsing problems, , which provides a set of optimal transitions for every valid parser ... See full document

... lected the first dataset of this task, ALG514, which contained 514 samples. They brought out a two- step pipeline model, which first used a classifier to select a template and then mapped the numbers into the slots. One ... See full document

... We have presented our approach to NMT decod- ing with terminology constraints using decoder at- tentions which enables reduced output duplication and better constraint placement compared to ex- isting methods. Our ... See full document

... the neural tissues as it does not require any incision on the nerve ...the neural activity in different ...peripheral neural interface (RPNI) [118], which uses a muscle graft to wrap around severed ... See full document

... like tree data, negative knowledge is less important and dangerous than positive knowledge; there are a few values associated with an entity but numerous that are ... See full document

... observed tree-structured data containing a large number of vertices using the distributional properties of the Continuum Random Tree (CRT) introduced by Aldous ... See full document