[PDF] Top 20 Data Recombination for Neural Semantic Parsing
Has 10000 "Data Recombination for Neural Semantic Parsing" found on our website. Below are the top 20 most common "Data Recombination for Neural Semantic Parsing".
Data Recombination for Neural Semantic Parsing
... In this paper, we only explore learning from log- ical forms. In the last few years, there has an emergence of semantic parsers learned from de- notations (Clarke et al., 2010; Liang et al., 2011; Berant et al., ... See full document
11
Weakly Supervised Neural Semantic Parsing with a Generative Ranker
... The vanilla NPR model is optimized with con- sistent logical forms which lead to correct de- notations. Although it achieves competitive re- sults compared to chart-based parsers, the train- ing of this model can be ... See full document
12
Linguistic Information in Neural Semantic Parsing with Multiple Encoders
... We now compare our best models to previous parsers 4 (Bos, 2015; Van Noord, Abzianidze, Toral, and Bos, 2018) and two baseline systems, SPAR and SIM - SPAR . As previously indicated, Van Noord, Abzianidze, Toral, and Bos ... See full document
8
Neural Semantic Parsing over Multiple Knowledge bases
... train semantic parsers over multiple knowledge-bases (KBs), while sharing in- formation across ...improve parsing ac- curacy by training a single sequence-to- sequence model over multiple KBs, when ... See full document
6
AdaNSP: Uncertainty driven Adaptive Decoding in Neural Semantic Parsing
... between semantic tokens and lexical meaning of natural language ...Recently neural semantic parsers, especially under the encoder-decoder framework, also sprang up (Dong and Lapata, 2016, 2018; Jia ... See full document
6
Compositional pre training for neural semantic parsing
... the parsing and to- ken accuracy reported for three standard semantic parsing datasets in (Jia and Liang, ...2016). Parsing accuracy is defined as the proportion of the pre- dicted logical ... See full document
7
Second Order Semantic Dependency Parsing with End to End Neural Networks
... training data, we repeated our experiments with randomly sampled 70%, 40% and 10% of the training ...training data becomes ...training data to learn this capability than a high-order ...training ... See full document
10
Grammatical Sequence Prediction for Real Time Neural Semantic Parsing
... for semantic parsing, where we observe that grammars often exist which specify valid formal representations of utterance ...up semantic pars- ...same neural model without grammatical ... See full document
10
Neural Semantic Parsing with Type Constraints for Semi Structured Tables
... 14,152 examples and the test set consists of 4,344 examples. The training set comes divided into 5 cross-validation folds for development using an 80/20 split. All data sets are constructed so that the development ... See full document
11
Neural Maximum Subgraph Parsing for Cross Domain Semantic Dependency Analysis
... the data-driven dependency parsing approach: decoding and dis- ...or semantic analysis into bilexical de- pendencies can be categorized into two domi- nant types: transition-based (Zhang et ... See full document
11
Semantic graph parsing with recurrent neural network DAG grammars
... a semantic bank where sentences in English, Italian, German, and Dutch have been annotated following Dis- course Representation Theory (Kamp and Reyle, ...the data and the grammar extracted from ... See full document
10
Neural Shift Reduce CCG Semantic Parsing
... CCG parsing largely relies on two types of ac- tions: using a lexicon to map words to their cate- gories, and combining categories to acquire the cat- egories of larger ...most semantic pars- ing ... See full document
12
Transfer Learning for Neural Semantic Parsing
... semantic parsing. However, because of the limit amount of annotated data, the advantage of neural networks to capture complex data representation using deep structure (Johnson et ... See full document
9
Dealing with Co reference in Neural Semantic Parsing
... We proposed three methods to handle co-indexed variables for neural semantic (AMR) parsing. The best results were obtained by the Indexing method, which explicitly encodes co-indexing nodes in the ... See full document
9
Neural Architectures for Multilingual Semantic Parsing
... Recent progress in multilingual NLP has moved towards building a unified model that can work across different languages, such as in multilingual dependency parsing (Ammar et al., 2016), multi- lingual MT (Firat et ... See full document
7
Confidence Modeling for Neural Semantic Parsing
... The model’s parameters or structures contain un- certainty, which makes the model less confident about the values of p ( a|q ). For example, noise in the training data and the stochastic learning algo- rithm itself ... See full document
11
Coarse to Fine Decoding for Neural Semantic Parsing
... Semantic parsing aims at mapping natural language utterances into structured mean- ing ...structure-aware neural architecture which decomposes the semantic parsing process into two ... See full document
12
Addressing the Data Sparsity Issue in Neural AMR Parsing
... dependency parsing has achieved high accuracy recently and can be trained on larger ...utilizes semantic role labeling and complex features, which makes the training process a long ... See full document
10
Parsing Syntactic and Semantic Dependencies with Two Single Stage Maximum Entropy Models
... We use a shift-reduce scheme to implement syn- tactic dependency parsing as in (Nivre, 2003). It takes a step-wised, history- or transition-based ap- proach. It is basically a word-by-word method with a projective ... See full document
5
Unsupervised Semantic Parsing
... Evaluating unsupervised semantic parsers is dif- ficult, because there is no predefined formal lan- guage or gold logical forms for the input sen- tences. Thus the best way to test them is by using them for the ... See full document
10
Related subjects