Towards Programming in Natural Language: Learning New Functions from Spoken Utterances
| Autor: | Tobias Hey, Sebastian Weigelt, Walter F. Tichy, Vanessa Steurer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Linguistics and Language Computer Networks and Communications Computer science Natural language understanding 02 engineering and technology Programming in natural language computer.software_genre Semantics Convolutional neural network conversational interfaces computational linguistics Naive Bayes classifier 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering natural language processing intelligent systems business.industry DATA processing & computer science Intelligent decision support system end-user programming neural networks artificial intelligence spoken language understanding Computer Science Applications machine learning naturalistic programming 020201 artificial intelligence & image processing natural language understanding Artificial intelligence ddc:004 Computational linguistics business computer Software Natural language Natural language processing Utterance Information Systems |
| Zdroj: | International journal of semantic computing, 14 (2), 249–272 |
| ISSN: | 1793-351X 1793-7108 |
| DOI: | 10.5445/ir/1000124405 |
| Popis: | Systems with conversational interfaces are rather popular nowadays. However, their full potential is not yet exploited. For the time being, users are restricted to calling predefined functions. Soon, users will expect to customize systems to their needs and create own functions using nothing but spoken instructions. Thus, future systems must understand how laypersons teach new functionality to intelligent systems. The understanding of natural language teaching sequences is a first step toward comprehensive end-user programming in natural language. We propose to analyze the semantics of spoken teaching sequences with a hierarchical classification approach. First, we classify whether an utterance constitutes an effort to teach a new function or not. Afterward, a second classifier locates the distinct semantic parts of teaching efforts: declaration of a new function, specification of intermediate steps, and superfluous information. For both tasks we implement a broad range of machine learning techniques: classical approaches, such as Naïve Bayes, and neural network configurations of various types and architectures, such as bidirectional LSTMs. Additionally, we introduce two heuristic-based adaptations that are tailored to the task of understanding teaching sequences. As data basis we use 3168 descriptions gathered in a user study. For the first task convolutional neural networks obtain the best results (accuracy: 96.6%); bidirectional LSTMs excel in the second (accuracy: 98.8%). The adaptations improve the first-level classification considerably (plus 2.2% points). |
| Databáze: | OpenAIRE |
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