ﻻ يوجد ملخص باللغة العربية
Over the past thirty years, there has been considerable progress in the design of natural language interfaces to databases. Most of this work has concerned snapshot databases, in which there are only limited facilities for manipulating time-varying information. The database community is becoming increasingly interested in temporal databases, databases with special support for time-dependent entries. We have developed a framework for constructing natural language interfaces to temporal databases, drawing on research on temporal phenomena within logic and linguistics. The central part of our framework is a logic-like formal language, called TOP, which can capture the semantics of a wide range of English sentences. We have implemented an HPSG-based sentence analyser that converts a large set of English queries involving time into TOP formulae, and have formulated a provably correct procedure for translating TOP expressions into queries in the TSQL2 temporal database language. In this way we have established a sound route from English to a general-purpose temporal database language.
Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a users natural language questions for question-answering (QA). Because data can
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 compl
Most existing natural language database interfaces (NLDBs) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (ve
Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than worrying
Recently, large pre-trained neural language models have attained remarkable performance on many downstream natural language processing (NLP) applications via fine-tuning. In this paper, we target at how to further improve the token representations on