[PDF] Top 20 Transfer Learning for Neural Semantic Parsing
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Transfer Learning for Neural Semantic Parsing
... our transfer learning frame- work, which has 4480 training and 448 test utter- ances (Zettlemoyer and Collins, ...constituency parsing as the large task, similar to the corpus in Vinyals et ...fixed ... See full document
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Coarse to Fine Decoding for Neural Semantic Parsing
... recently, neural sequence-to-sequence models have been applied to semantic parsing with promising results (Dong and Lapata, 2016; Jia and Liang, 2016; Ling et ...2016), transfer ... See full document
12
Compositional pre training for neural semantic parsing
... Moreover, building annotated semantic parsing datasets is highly labor-intensive and parsers built for one domain do not necessarily transfer across domains. Fan et al. propose a multi-task setup and ... See full document
7
Bayesian Learning for Neural Dependency Parsing
... for parsing in the small data regime have been ...deep neural networks (DNNs) introduces statistical challenges at both estimation (training), due to the risk of overfitting, and at test time as the model ... See full document
11
Learning a Lexicon for Broad coverage Semantic Parsing
... on learning semantic parsers for specific task/ domains, the results don’t transfer from one domain to another ...broad-coverage semantic lexicon for domain independent semantic ...a ... See full document
6
Robust Incremental Neural Semantic Graph Parsing
... AMR parsing by introducing structure that is present explicitly in MRS but not in AMR (Buys and Blunsom, ...dependency parsing (Dyer et ...constituency parsing (Vinyals et ...deep learning is ... See full document
12
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
Neural Semantic Parsing over Multiple Knowledge bases
... Learning a semantic parser involves mapping lan- guage phrases to KB constants, as well as learning how language composition corresponds to logical form composition. We hypothesized that the main ... See full document
6
Learning Programmatic Idioms for Scalable Semantic Parsing
... these neural encoder-decoder models with the ability to decode in terms of frequently oc- curring higher level idiomatic structures to achieve gains in accuracy and training ... See full document
10
Data Recombination for Neural Semantic Parsing
... explore learning from log- ical ...of semantic parsers learned from de- notations (Clarke et ...attention-based neural machine trans- lation models (Bahdanau et ... See full document
11
Weakly Supervised Neural Semantic Parsing with a Generative Ranker
... Recently, neural semantic parsing has attracted a great deal of ...reinforcement learning to train neural semantic parsers from question- answer pairs (Liang et ...ral ... See full document
12
Integrated Learning of Dialog Strategies and Semantic Parsing
... using neural networks (Mrkˇsi´c et ...a semantic parser that can be incre- mentally updated from a small number of interac- tions is likely to perform ... See full document
11
Deep Multitask Learning for Semantic Dependency Parsing
... deep neural architecture that parses sentences into three semantic de- pendency graph ...for semantic dependency parsing, without using hand-engineered features or ...multitask learning ... See full document
12
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
Dealing with Co reference in Neural Semantic Parsing
... in learning the paths, suggesting that the baseline results might merely be an effect of sparse ...data, learning such sophisticated structures is too difficult for end-to-end sequence-to- sequence models ... See full document
9
Semantic graph parsing with recurrent neural network DAG grammars
... The advantage of predicting linearized graphs is twofold. The first advantage is that graph- bank datasets usually already contain lineariza- tions, which can be used without additional work. These linearizations are ... See full document
10
Learning Structured Natural Language Representations for Semantic Parsing
... 3.1 Generating Ungrounded Representations At this stage, utterances are mapped to interme- diate representations with a transition-based algo- rithm. In general, the transition system generates the representation by ... See full document
12
Neural Semantic Parsing with Type Constraints for Semi Structured Tables
... make learning difficult. Another approach is to treat se- mantic parsing as a machine translation problem, where the logical form is linearized then predicted as an unstructured sequence of tokens (Andreas ... See full document
11
Confidence Modeling for Neural Semantic Parsing
... for semantic parsing (Dong and Lapata, 2016; Jia and Liang, 2016; Ling et ...fer learning (Fan et al., 2017). Current semantic parsers will by default generate some output for a given input ... See full document
11
Neural Architectures for Multilingual Semantic Parsing
... task learning framework, motivated by its success in other fields, ...e.g., neural machine translation (MT) (Dong et ...generating semantic repre- ...during parsing; ... See full document
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