[PDF] Top 20 Coarse to Fine Decoding for Neural Semantic Parsing
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Coarse to Fine Decoding for Neural Semantic Parsing
... Semantic parsing maps natural language utter- ances onto machine interpretable meaning rep- resentations ...recurrent neural networks to a variety of NLP tasks (Bah- danau et ...treat semantic ... See full document
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Generating Long and Informative Reviews with Aspect Aware Coarse to Fine Decoding
... view generation has been proposed to assist the writing of online reviews for users. RNN-based methods have been proposed to generate the re- view content conditioned on useful context infor- mation (Tang et al., 2016; ... See full document
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Reranking for Neural Semantic Parsing
... a semantic coher- ence score based on the latent pairwise alignment between tokens in x and ...the semantic equivalence of an utterance x and an MR z based on pair-wise associations of tokens in x and z in ... See full document
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Confidence Modeling for Neural Semantic Parsing
... for semantic parsing (Dong and Lapata, 2016; Jia and Liang, 2016; Ling et ...2017), coarse-to- fine decoding (Dong and Lapata, 2018), network sharing (Susanto and Lu, 2017; Herzig and Berant, ... See full document
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AdaNSP: Uncertainty driven Adaptive Decoding in Neural Semantic Parsing
... Our model outperforms the other comparative neural semantic parsers on this two set. We reuse the data from Dong and Lapata (2018) since the datasets are identical. Results are listed in Table 1. Our ... See full document
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Neural Semantic Parsing with Type Constraints for Semi Structured Tables
... recent neural semantic parsers (Jia and Liang, 2016; Dong and Lapata, 2016). The seq2tree model improves on the seq2seq model by includ- ing an action for generating matched parenthe- ses, then recursively ... See full document
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Incremental and Multi-Task Learning Strategies for Coarse-To-Fine Semantic Segmentation
... The semantic understanding of a scene is a key problem in the computer vision ...multi-level semantic segmentation task where a deep neural network is first trained to recognize an initial, ... See full document
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Robust Incremental Neural Semantic Graph Parsing
... MRS makes an explicit distinction between sur- face and abstract predicates (by convention surface predicates are prefixed by an underscore). Surface predicates consist of a lemma followed by a coarse ... See full document
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Neural Semantic Parsing over Multiple Knowledge bases
... Results show (Table 1) that training on multi- ple KBs improves average accuracy over all do- mains for all our proposed models, and that per- formance improves as more parameters are shared. Our strongest results come ... See full document
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Learning Programmatic Idioms for Scalable Semantic Parsing
... constrained decoding mecha- nisms to generate syntactically correct output us- ing a decoder that is either grammar-aware or has a dynamically determined modular structure par- alleling the structure of the ... See full document
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Compositional pre training for neural semantic parsing
... Semantic parsing is the process of translating natural language utterances into logical forms, which has many important applications such as question answering and instruction follow- ...to fine-tune ... See full document
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Semantic Proto Roles
... more fine-grained properties that have a prototype structure – the Proto-role Hypoth- ...the fine-grained properties that make up ...in fine-grained properties across tokens of a verb in a corpus ... See full document
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Linguistic Information in Neural Semantic Parsing with Multiple Encoders
... We employ a recurrent sequence-to-sequence neural network with attention (Bahdanau et al., 2014) and two bi-LSTM layers, similar to the one used by Van Noord, Abzianidze, Toral, and Bos (2018). However, their ... See full document
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Grammatical Sequence Prediction for Real Time Neural Semantic Parsing
... plex constraints, the automaton can recognize the result by taking one or two of the vertical paths (state sequences 2-6-7 and 3-8-9, respectively). These paths can also accept strings that are not syntactically valid ... See full document
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Weakly Supervised Neural Semantic Parsing with a Generative Ranker
... Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as ...a neural parser-ranker system for weakly-supervised semantic ...our semantic parser, ... See full document
12
Direct Conversion RF Digital Receiver in L-Band for QPSK
... the coarse frequency subsystem can be tuned to see the effect of estimation accuracy and the tolerance to a high noise ...the coarse frequency compensation subsystem is low, then fine frequency ... See full document
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Multilingual Semantic Parsing : Parsing Multiple Languages into Semantic Representations
... multilingual semantic parsing where there are two, three, four and five input ...bilingual semantic parsing where we have two different input ... See full document
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Second Order Semantic Dependency Parsing with End to End Neural Networks
... To evaluate the performance of our model on smaller training data, we repeated our experiments with randomly sampled 70%, 40% and 10% of the training set. Table 3 shows the F1 scores averaged over 5 runs (each time with ... See full document
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Semantic Kernels for Semantic Parsing
... it with the string “::L”, where L is the first let- ter of the POS-tags of the words, e.g., along, my and route, receive i, p and n, which are the first letters of the POS-tags IN, PRN and NN, respec- tively. SK applied ... See full document
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Influence of Coarse Aggregate Shape Factors on Bituminous Mixtures
... Generally aggregate represents coarse and fine aggregates. There are various types of mineral aggregates which form the bituminous mixes. Aggregates play a very crucial role, which provides strength to SMA ... See full document
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