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Towards programming in natural language: Learning New Functions from Spoken Utterances

INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING 2020年第2期14卷 249-272页

作者： Weigelt, Sebastian Steurer, Vanessa Hey, Tobias Tichy, Walter F. Karlsruhe Inst Technol Inst Program Struct & Data Org Karlsruhe Germany

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 Naive 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).

关键词： programming in natural language natural language understanding end-user programming conversational interfaces spoken language understanding natural language processing computational linguistics naturalistic programming machine learning neural networks intelligent systems artificial intelligence

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NLCI: a natural language command interpreter

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AUTOMATED SOFTWARE ENGINEERING 2017年第4期24卷 839-861页

作者： Landhaeusser, Mathias Weigelt, Sebastian Tichy, Walter F. Karlsruhe Inst Technol Inst Program Struct & Data Org Karlsruhe Germany

natural language interfaces are becoming more and more common, because they are powerful and easy to use. Examples of such interfaces are voice controlled navigation devices, Apple's personal assistant Siri, Google Voice Search, and translation services. However, such interfaces are extremely difficult to build, to maintain, and to port to new domains. We present an approach for building and porting such interfaces quickly. NLCI is a natural language command interpreter that accepts action commands in English and translates them into executable code. The core component is an ontology that models an API. Once the API is "ontologized", NLCI translates input sentences into sequences of API calls that implement the intended actions. Two radically different APIs were ontologized: openHAB for home automation and Alice for building 3D animations. Construction of the ontology can be automated if the API uses descriptive names for its components. In that case, the language interface can be generated completely automatically. Recall and precision of NLCI on a benchmark of 50 input scripts are 67 and 78 %, resp. Though not yet acceptable for practical use, the results indicate that the approach is feasible. NLCI accepts typed input only. Future work will use a speech front-end to test spoken input.

关键词： programming in natural language End-user programming Knowledge-based software engineering Program synthesis natural language processing for software engineering

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Dialog-based Meaning Derivation Service For Technical language Domains 13

Dialog-based Meaning Derivation Service For Technical Langua...

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13th IEEE International Conference on Semantic Computing (ICSC)

作者： Wachtel, Alexander Schulz, Sophie Tichy, Walter F. Karlsruhe Inst Technol Chair Programming Syst Prof Walter Tichy Karlsruhe Germany

ISBN: (纸本)9781538667835

In our previous work, we present on how new algorithms can be recognized and learned from human descriptions. In this case, end users are able to extend the given system by their own functionality. During the evaluation, due to language limits on the system, it could interpret only 59% of user input correctly. In this paper, we provide an approach on the Dialog-based Meaning Derivation Service (DMDS). In case, user input does not match to the system knowledge, DMDS serves various word networks to find relevant synonyms as candidates for the unknown word. DMDS then tries to verify these candidates to the given knowledge base. Finally, matched candidates are presented to the end user by the dialog system for confirmation of a contextual match. The meaning is learned after the user confirmation and is mapped to the given functionality, and can be used afterwards. Therefore, the model developed in this work can be categorized as supervised learning. Finally, both the performance and the quality recorded by input from a user study were examined. However, DMDS improves the correct interpretation of the system from 59% to 82%. Our focus is to improve the interaction between humans and machines and enable the end user to instruct programmable devices, without having to learn a programming language.

关键词： Dialog Systems Dialog-based Meaning Derivation Knowledge Base programming in natural language

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Integrating a Dialog Component into a Framework for Spoken language Understanding 6

Integrating a Dialog Component into a Framework for Spoken L...

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6th IEEE/ACM International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)

作者： Weigelt, Sebastian Hey, Tobias Landhaeusser, Mathias Karlsruhe Inst Technol Karlsruhe Germany ThingsTHINKING GmbH Karlsruhe Germany

ISBN: (纸本)9781450357234

Spoken language interfaces are the latest trend in human computer interaction. Users enjoy the newly found freedom but developers face an unfamiliar and daunting task. Creating reactive spoken language interfaces requires skills in natural language processing. We show how a developer can integrate a dialog component in a natural language processing system by means of software engineering methods. Our research project PARSE that aims at naturalistic end-user programming in spoken natural language serves as an example. We integrate a dialog component with PARSE without affecting its other components: We modularize the dialog management and introduce dialog acts that bundle a trigger for the dialog and the reaction of the system. We implemented three dialog acts to address the following issues: speech recognition uncertainties, coreference ambiguities, and incomplete conditionals. We conducted a user study with ten subjects to evaluate our approach. The dialog component achieved resolution rates from 23% to 50% (depending on the dialog act) and introduces a negligible number of errors. We expect the overall performance to increase even further with the implementation of additional dialog acts.

关键词： programming in natural language naturalistic programming end-user programming knowledge-based software engineering natural language processing for software engineering dialog systems dialog integration human-computer interaction

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Detection of Conditionals in Spoken Utterances 12

Detection of Conditionals in Spoken Utterances

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12th IEEE International Conference on Semantic Computing (ICSC)

作者： Weigelt, Sebastian Hey, Tobias Steurer, Vanessa Karlsruhe Inst Technol Inst Program Struct & Data Org Karlsruhe Germany

ISBN: (纸本)9781538644072

State-of-the-art intelligent assistant systems such as Siri & Co. struggle with conditionals. They reliably react to ordinary commands. However, their architectures are not designed to cope with complex conditional queries. We propose a system to overcome these limitations. Our approach models if-then-else constructs in spoken utterances explicitly. The model bridges the gap between linguistic and programmatic semantics. To proof our concept, we apply a rule-based approach to extract conditionals. For our prototype we use part-of-speech and chunk tags provided by NLP tools. We make use of coreference information to determine the reference frame of a condition. The explicit modeling of conditionals allows us to evaluate the accuracy of our approach independently from other language understanding tasks. The prototype works well in the domain of humanoid robotics. In a user study we achieve F-1 scores of 0.783 (automatic speech recognition) up to 0.898 (manual transcripts) on unrestricted utterances.

关键词： Condition Model Conditional Semantics Conditionals End User programming Knowledge Representation language Model natural language Processing natural language Understanding programming in natural language Semantic Parsing Spoken language Interfaces Spoken language Understanding

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Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems. 14

Roger that! Learning How Laypersons Teach New Functions to I...

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14th IEEE International Conference on Semantic Computing (ICSC)

作者： Weigelt, Sebastian Steurer, Vanessa Hey, Tobias Tichy, Walter F. Karlsruhe Inst Technol Inst Program Struct & Data Org D-76021 Karlsruhe Germany

ISBN: (纸本)9781728163321

Intelligent systems are rather smart today but still limited to built-in functionality. To break through this barrier, future systems must allow users to easily adapt the system by themselves. For humans the most natural way to communicate is talking. But what if users want to extend the systems' functionality with nothing but natural language? Then intelligent systems must understand how laypersons teach new skills. To grasp the semantics of such teaching sequences, we have defined a hierarchical classification task. On the first level, we consider the existence of a teaching intent in an utterance;on the second, we classify the distinct semantic parts of teaching sequences: declaration of a new function, specification of intermediate steps, and superfluous information. We evaluate twelve machine learning techniques with multiple configurations tailored to this task ranging from classical approaches such as naive-bayes to modern techniques such as bidirectional LSTMs and task-oriented adaptations. On the first level convolutional neural networks achieve the best accuracy (96.6%). For the second task, bidirectional LSTMs are the most accurate (98.8%). With the additional adaptations we are able to improve both classifications distinctly (up to 1.8%).

关键词： Artificial neural networks Convolutional neural networks End user programming Intelligent system Machine learning natural language processing natural language understanding Neural networks programming in natural language Recurrent neural networks

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NLCI – A natural language Command Interpreter

NLCI – A Natural Language Command Interpreter

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Software Engineering 2017

作者： Landhäußer, Mathias Weigelt, Sebastian Tichy, Walter F. Karlsruhe Institute of Technology Institute for Program Structures and Data Organization Am Fasanengarten 5 Karlsruhe76131 Germany

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Context Model Acquisition from Spoken Utterances

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2017年第9-10期27卷 1439-1453页

作者： Weigelt, Sebastian Hey, Tobias Tichy, Walter F. Karlsruhe Inst Technol Inst Program Struct & Data Org D-76131 Karlsruhe Germany

Current systems with spoken language interfaces do not leverage contextual information. Therefore, they struggle with understanding speakers' intentions. We propose a system that creates a context model from user utterances to overcome this lack of information. It comprises eight types of contextual information organized in three layers: individual, conceptual, and hierarchical. We have implemented our approach as a part of the project PARSE. It aims at enabling laypersons to construct simple programs by dialog. Our implementation incrementally generates context including occurring entities and actions as well as their conceptualizations, state transitions, and other types of contextual information. Its analyses are knowledge- or rule-based (depending on the context type), but we make use of many well-known probabilistic NLP techniques. In a user study we have shown the feasibility of our approach, achieving F-1 scores from 72% up to 98% depending on the type of contextual information. The context model enables us to resolve complex identity relations. However, quantifying this effect is subject to future work. Likewise, we plan to investigate whether our context model is useful for other language understanding tasks, e.g. anaphora resolution, topic analysis, or correction of automatic speech recognition errors.

关键词： Spoken language interfaces spoken language understanding language model programming in natural language end-user programming ontologies natural language processing knowledge representation context model context natural language understanding

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