[PDF] Top 20 Reranking for Neural Semantic Parsing
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Reranking for Neural Semantic Parsing
... of reranking results in Table ...for semantic pars- ing tasks like A TIS , the reconstruction model per- forms generally better on two Python code genera- tion tasks, where target MRs are much more com- ... See full document
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Linguistic Information in Neural Semantic Parsing with Multiple Encoders
... Sequence-to-sequence neural networks have shown remarkable performance in semantic parsing (Ling et ...of semantic phenomena, usually without resorting to any linguistic information such as ... See full document
8
Weakly Supervised Neural Semantic Parsing with a Generative Ranker
... weakly-supervised neural semantic parsing system which aims to handle both ...inverse neural parser measures the degree to which the logical form represents the meaning of the utter- ... 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 benefit of ... See full document
6
Second Order Semantic Dependency Parsing with End to End Neural Networks
... powerful neural network for semantic dependency parsing using a bilinear or biaffine (Dozat and Manning, 2016) layer to encode the interaction between ...for semantic dependency ... See full document
10
Neural Maximum Subgraph Parsing for Cross Domain Semantic Dependency Analysis
... on semantic parsing focused on the in-domain setting, meaning that both train- ing and testing data are drawn from the same do- ...data-driven parsing system achieves a high in-domain accuracy, it ... See full document
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Discriminative Reranking for Natural Language Parsing
... or neural networks, it is generally not feasible to perform an exhaustive search (with O(m) time complexity) for the feature which has the greatest impact on the exponential 7 loss ...or neural network ... See full document
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Compositional pre training for neural semantic parsing
... language. Semantic parsing is different in that the decoded sequence need to be constrained by what would constitute a valid logical ...to semantic parsing sys- ...tasks semantic ... See full document
7
Neural Shift Reduce CCG Semantic Parsing
... Shift-reduce parsing is a class of parsing methods that guarantees a linear number of operations in sen- tence ...deep neural architectures for decision making in linear- time dependency ... See full document
12
Grammatical Sequence Prediction for Real Time Neural Semantic Parsing
... with neural models in a number of recent papers on seman- tic parsing (Yin and Neubig, 2017, 2018; Krish- namurthy et ...the neural network model and to restrict the de- cisions the model can make at ... See full document
10
Semantic Kernels for Semantic Parsing
... of semantic information conveyed by Brown Clusters (BCs) (Brown et ...and semantic similarity, while also combining them with innovative ...use reranking, similarly to (Saleh et ...the ... See full document
7
Neural Semantic Parsing with Type Constraints for Semi Structured Tables
... novel neural semantic parsing model that addresses these limitations of prior ...training neural semantic parsers from question-answer ... See full document
11
Semantic graph parsing with recurrent neural network DAG grammars
... semantic parsing. Our experiments re- vealed that universal semantic tags are most use- ful, while the multilingual word embeddings that we have tested with are ... See full document
10
AdaNSP: Uncertainty driven Adaptive Decoding in Neural Semantic Parsing
... The distinguishing difference of semantic pars- ing, however, is in its target sequences, which are token sequences of well-formed semantic rep- resentations. SQL language and lambda expres- sions are ... See full document
6
Confidence Modeling for Neural Semantic Parsing
... of neural se- mantic parsers which, contrary to more traditional methods, do not make use of lexicons or templates and as a result the sources of errors and inconsis- tencies are difficult to ...application, ... See full document
11
Data Recombination for Neural Semantic Parsing
... In semantic parsing, however, we would like to capture more than just invariance properties. Con- sider an example with the utterance “what states border texas ?”. Given this example, it should be easy to ... See full document
11
Neural Architectures for Multilingual Semantic Parsing
... In this paper, we address semantic pars- ing in a multilingual context. We train one multilingual model that is capable of pars- ing natural language sentences from mul- tiple different languages into their corre- ... See full document
7
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
... tups B → A outperform their base parsers B? Per- haps generative models A are simply superior to the base models B and direct generative parsing (A → A) would be better still if it were feasi- ble. If so, we would ... See full document
6
Discriminative Reranking for Semantic Parsing
... Two corpora of natural language sentences paired with MRs were used in the reranking experiments. For CL ANG , 300 pieces of coaching advice were randomly selected from the log files of the 2003 RoboCup Coach ... See full document
8
Transfer Learning for Neural Semantic Parsing
... Recent works have proven sequence-to- sequence to be an effective model architecture (Jia and Liang, 2016; Dong and Lapata, 2016) for semantic parsing. However, because of the limit amount of annotated ... See full document
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