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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Programming in Natural Language with fuSE : Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding ... Weigelt, Sebastian; Steurer, Vanessa; Hey, Tobias. - : Karlsruhe, 2020 BASE Show details 2 Towards Programming in Natural Language: Learning New Functions from Spoken Utterances ... Weigelt, Sebastian; Steurer, Vanessa; Hey, Tobias. - : World Scientific Publishing, 2020 BASE Show details 3 Towards Programming in Natural Language: Learning New Functions from Spoken Utterances Hey, Tobias; Weigelt, Sebastian; Steurer, Vanessa; Tichy, Walter F. In: International journal of semantic computing, 14 (2), 249–272 ; ISSN: 1793-351X, 1793-7108 (2020) Abstract: 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). Keyword: artificial intelligence; computational linguistics; conversational interfaces; DATA processing & computer science; ddc:004; end-user programming; info:eu-repo/classification/ddc/004; intelligent systems; machine learning; natural language processing; natural language understanding; naturalistic programming; neural networks; Programming in natural language; spoken language understanding URL: https://publikationen.bibliothek.kit.edu/1000124405 https://doi.org/10.5445/IR/1000124405/post https://publikationen.bibliothek.kit.edu/1000124405/130160664 BASE Hide details 4 Programming in Natural Language with fuSE : Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding Weigelt, Sebastian; Hey, Tobias; Tichy, Walter F.. - : Association for Computational Linguistics, 2020 BASE Show details

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