[PDF] Top 20 Natural Language to Structured Query Generation via Meta Learning

... We evaluate our model on the WikiSQL dataset (Zhong et al., 2017). We follow the data pre- processing in (Wang et al., 2017). Specifically, we first preprocess the dataset by running both tables and question-query ... See full document

... deep learning and semantic parsing has significantly im- proved the translation accuracy of natural language questions to structured ...based structured query generation ... See full document

... as: natural language understanding (NLU) (Deng et ...policy learning (Cuay´ahuitl et al., 2015; Young et al., 2013) and natural lan- guage generation (NLG) (Dhingra et ... See full document

... both natural language and se- mantics (Tseng et ...imitation learning to train NLG models, where the Locally Opti- mal Learning to Search framework was adopted to train against ... See full document

... grounded language learn- ing did not fully capture the semantics under- lying the correspondences between structured world state representations and texts, espe- cially those between numerical values and ... See full document

... Deep learning techniques for syntactic parsing were developed where they propagate information through the tree using recursive compositional procedure, proceeding step by step in a greedy ... See full document

... a structured dataset that integrates into the target KB as a fused queryable ...the generation of detailed ex- planations, thus being attractive to end ... See full document

... 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

... Before we can attempt to design and implement a question answering system, we need to address sev- eral key issues. First, we need to decide what knowl- edge our system needs. Specifically, we must decide what linguistic ... See full document

... Another type of models that do not use a formal grammar as basis in their parsing are graph-based models. While transition-based models try to locally find the best dependency relations, the graph-based models learn a ... See full document

... Baseline Clustering is a natural baseline model for our partitioning problem. In our experiments, we a employ a single-link agglomerative clustering al- gorithm that uses the scores returned by the maxi- mum ... See full document

... mashup language, called MashQL, which allows people to mash up data ...to query an RDF graph without any prior knowledge about its structure or technical details; as well as it supports pipelines and ... See full document

... proach to QA is to transform the question and knowledge into a compatible form, and perform reasoning to determine which fact in the KB an- swers a given question. Examples of this approach include pattern-based question ... See full document

... a learning scheme which provides the ability to jointly learn two mod- els for NLG and for NLU using large amount of unannotated data and small amount of anno- tated ... See full document

... (terminology query language), which encompassed SQL’s idea of universality ...RDF Query Language) in par- ticular has established itself in many areas as the de facto standard ...ternal ... See full document

... NATURAL LANGUAGE ACCESS TO STRUCTURED TEXT COLING 82, J Horeck~ {eft ) North Holland Publishing Company Cc~ Academt~ 1982 NATURAL LANGUAGE ACCESS TO STRUCTURED TEXT J e r r y R Robbs, Donald E W a l k[.] ... See full document

... In the second adaptation scheme, the multi- input model, the pretrained language model is duplicated in an encoder-decoder architecture (Fig. 1b). Similar to the single-input model, natu- ral separators, ... See full document

... processing, language generation, even in its most primitive form as canned text (Jurafsky and Martin, 2000), was an exciting thing to ...make language generation more adaptable to context ... See full document

... With the above formulation, the current problem is how to estimate the empirical marginal distri- bution P ˆ (·). To accurately estimate data distri- bution, the data properties should be considered, because different ... See full document

... AI techniques have sent huge waves across healthcare. Artificial Intelligence or Machine learning in current times serves as the primary choice for data mining and big data analysis. There is an active discussion ... See full document