We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general ...
Apr 27, 2017 · We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures.
We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utter- ances to intermediate, domain-general ...
This work proposes a novel dependency-based hybrid tree model for semantic parsing, which converts natural language utterance into machine interpretable ...
Jul 30, 2017 · We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, ...
Our problem is to learn a semantic parser that maps x to. G via an ... ○ A model which jointly learns how to parse natural language semantics and ...
4.2.5 CHENG17 [73] Sequence-to-sequence model for semantic parsing reduces the need for a domain-specific assumption, grammar learning, and more expensive ...
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Aug 22, 2023 · The dissertation consists of three parts. The first part introduces a general-purpose parsing model with built-in syntactic knowledge of the ...
Sep 9, 2019 · Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution ...
Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real ...