Нейронный семантический парсер

method ID: neuralsemanticparser

Related Papers

From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractOur goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, …...

40021

AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractNeural semantic parsers utilize the encoder-decoder framework to learn an end-to-end model for semantic parsing that transduces a natural language sentence to the formal semantic representation. To keep the model …...

39545

Learning an Executable Neural Semantic Parser

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractThis article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a …...

41428

Reranking for Neural Semantic Parsing

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractSemantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs). While neural network-based semantic parsers have achieved impressive improvements over previous methods, results …...

39515

Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractClinical trials often require that patients meet eligibility criteria (e.g., have specific conditions) to ensure the safety and the effectiveness of studies. However, retrieving eligible patients for a trial from …...

14128

SPARQLing Database Queries from Intermediate Question Decompositions

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractTo translate natural language questions into executable database queries, most approaches rely on a fully annotated training set. Annotating a large dataset with queries is difficult as it requires query-language …...

30999

Exploring the Secrets Behind the Learning Difficulty of Meaning Representations for Semantic Parsing

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPrevious research has shown that the design of Meaning Representation (MR) greatly influences the final model performance of a neural semantic parser. Therefore, designing a good MR is a long-term …...

31146

Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractEven though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, …...

4858

Does Character-level Information Always Improve DRS-based Semantic Parsing?

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractEven in the era of massive language models, it has been suggested that character-level representations improve the performance of neural models. The state-of-the-art neural semantic parser for Discourse Representation Structures …...

26414

Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper studies a new task of federated learning (FL) for semantic parsing, where multiple clients collaboratively train one global model without sharing their semantic parsing data. By leveraging data …...

37938

Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractNearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion. Though such systems have achieved impressive results across a variety of datasets and domains, recent …...

38352

Program Synthesis for Complex QA on Charts via Probabilistic Grammar Based Filtered Iterative Back-Translation

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractAnswering complex reasoning questions from chart images is a challenging problem requiring a combination of natural language understanding, fine-grained perception, and analytical reasoning. Current chart-based Question Answering (QA) approaches largely …...

33262