speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets in our daily lives, since voice is the most popular and efficient way for human-computer interaction. This pape...
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speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets in our daily lives, since voice is the most popular and efficient way for human-computer interaction. This paper works toward designing more effective speech-based interfaces to query the structured data in relational databases. We first identify a new task named speech-to-sql, which aims to understand the information conveyed by human speech and directly translate it into structured query language (sql) statements. A naive solution to this problem can work in a cascaded manner, that is, an automatic speech recognition component followed by a text-to-sql component. However, it requires a high-quality ASR system and also suffers from the error compounding problem between the two components, resulting in limited performance. To handle these challenges, we propose a novel end-to-end neural architecture named speechsqlNet to directly translate human speech into sql queries without an external ASR step. speechsqlNet has the advantage of making full use of the rich linguistic information presented in speech. To the best of our knowledge, this is the first attempt to directly synthesize sql based on common natural language questions in spoken form, rather than a natural language-based version of sql. To validate the effectiveness of the proposed problem and model, we further construct a dataset named speechQL, by piggybacking the widely used text-to-sql datasets. Extensive experimental evaluations on this dataset show that speechsqlNet can directly synthesize high-quality sql queries from human speech, outperforming various competitive counterparts as well as the cascaded methods in terms of exact match accuracies. We expect speech-to-sql would inspire more research on more effective and efficient human-machine interfaces to lower the barrier of using relational databases.
This paper presents the development process of a natural language to sql model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, ha...
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ISBN:
(纸本)9798350358810;9798350358803
This paper presents the development process of a natural language to sql model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73% and 84% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.
With recent development in natural language processing (NLP) and automatic speech recognition (ASR), voice-based interfaces have become a necessity for applications such as chatbots, search engines, and databases. In ...
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ISBN:
(纸本)9781450392495
With recent development in natural language processing (NLP) and automatic speech recognition (ASR), voice-based interfaces have become a necessity for applications such as chatbots, search engines, and databases. In this demonstration, we introduce VOICEQITERYSYSTEM, a voice-based database querying system that enables users to conduct data operations with natural language questions (NLQs). Different from existing voice-based interfaces such as SpeakQL or EchoQuery, which restricts the voice input to be an exact sql or follow a pre-defined template, VOICEQUERYSYSTEM attempts to achieve data manipulation via common NLQs, and thus does not require the user's technical background in sql language. The underlying techniques in VOICEQITERYSYSTEM is a new task named speech-to-sql, which aims to understand the semantic in speech and then translate it into sql queries. We explore two proposed approaches - the cascaded one and the end-to-end (E2E) one towards speech-to-sql translation. The cascaded method first converts the user's voice-based NLQs into text by a self-developed ASR module, and then conducts downstream sql generation via a text-to-sql model (i.e., IRNet). In contrast, the E2E method is a novel neural architecture named speechsqlNet designed by us, which converts the speech signals into sql queries directly without the middle medium as text. Extensive experiments and demonstrations validate the rationale of the speech-to-sql task and the effectiveness of the proposed speechsqlNet model. To the best of our knowledge, this is the first system that provides a voice-based querying functionality on DBMS from common NLQs.
With increasing complexity and volume of collected data continuing to rise, it is becoming ever more important to develop systems with high interactability. Businesses with an interest in big data continue to seek sol...
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ISBN:
(纸本)9781665434416
With increasing complexity and volume of collected data continuing to rise, it is becoming ever more important to develop systems with high interactability. Businesses with an interest in big data continue to seek solutions that limit cost while providing effective, simplified solutions to current issues in data retrieval. Combined analysis and application of a multi-factorial system will likely lead to promising results in ease of reporting of complex data by nontechnical end users. This survey is focused on natural language processing (NLP) implementations for data query systems, especially related to massive data sets (1TB+) in OLTP databases, OLAP databases, and data warehouses. We are seeking the most up-to-date and effective uses of NLP for speech-to-sql and Text-to-sql generation, and the most recent advancements in data warehousing to optimize ELT efficiency and data retrieval, focusing on the highest performing code implementations on the Spider and Wikisql datasets. Many models, including sequence-to-sequence (seq2seq), sequence-to-sql (Seq2sql), and fuzzy semantic to sql (F-Semtosql), among others, are briefly described and compared. As well, recent advancements in data warehousing technology like multi-disk buffering in the ELT process and hybrid multi-dimensional and relational OLAP databases (HOLAPs) are discussed. The learning gathered here is applied to fill a gap in the current industrial knowledge base in service of increased efficiency in data access, retrieval, and reporting in a customer-facing environment.
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