903 research outputs found

    SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

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    In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

    The Speech-Language Interface in the Spoken Language Translator

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    The Spoken Language Translator is a prototype for practically useful systems capable of translating continuous spoken language within restricted domains. The prototype system translates air travel (ATIS) queries from spoken English to spoken Swedish and to French. It is constructed, with as few modifications as possible, from existing pieces of speech and language processing software. The speech recognizer and language understander are connected by a fairly conventional pipelined N-best interface. This paper focuses on the ways in which the language processor makes intelligent use of the sentence hypotheses delivered by the recognizer. These ways include (1) producing modified hypotheses to reflect the possible presence of repairs in the uttered word sequence; (2) fast parsing with a version of the grammar automatically specialized to the more frequent constructions in the training corpus; and (3) allowing syntactic and semantic factors to interact with acoustic ones in the choice of a meaning structure for translation, so that the acoustically preferred hypothesis is not always selected even if it is within linguistic coverage.Comment: 9 pages, LaTeX. Published: Proceedings of TWLT-8, December 199

    GEMINI: A Natural Language System for Spoken-Language Understanding

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    Gemini is a natural language understanding system developed for spoken language applications. The paper describes the architecture of Gemini, paying particular attention to resolving the tension between robustness and overgeneration. Gemini features a broad-coverage unification-based grammar of English, fully interleaved syntactic and semantic processing in an all-paths, bottom-up parser, and an utterance-level parser to find interpretations of sentences that might not be analyzable as complete sentences. Gemini also includes novel components for recognizing and correcting grammatical disfluencies, and for doing parse preferences. This paper presents a component-by-component view of Gemini, providing detailed relevant measurements of size, efficiency, and performance.Comment: 8 pages, postscrip

    Spoken language processing in the hybrid connectionist architecture SCREEN

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    In this paper we describe a robust, learning approach to spoken language understanding. Since interactively spoken and computationally analyzed language often contains many errors, robust connectionist networks are used for providing a flat screening analysis. A screening analysis is a shallow flat analysis based on category sequences at various syntactic, semantic and dialog levels. Rather than using tree or graph representations a screening analysis uses category sequences in order to support robustness and learning. This flat screening analysis is examined in the context of the system SCREEN (Symbolic Connectionist Robust EnterprisE for Natural language). Starting with the word hypotheses generated by a speech recognizer, we give an overview of the architecture, and illustrate the flat robust processing at the levels of syntax, semantics, and dialog acts. While early connectionist models were often limited to a single network and a small task, the hybrid connectionist SCREEN system is an important step towards exploring connectionist techniques in larger hybrid symbolic/connectionist environments and for real-world problemsBased on our experience with SCREEN, hybrid connectionist techniques show a lot of potential for supporting robustness in interactive spoken language processing

    Temporal patterns of error repairs and approrpiateness repairs in Hungarian

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    Co-constructing intersubjectivity with artificial conversational agents: people are more likely to initiate repairs of misunderstandings with agents represented as human

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    This article explores whether people more frequently attempt to repair misunderstandings when speaking to an artificial conversational agent if it is represented as fully human. Interactants in dyadic conversations with an agent (the chat bot Cleverbot) spoke to either a text screen interface (agent's responses shown on a screen) or a human body interface (agent's responses vocalized by a human speech shadower via the echoborg method) and were either informed or not informed prior to interlocution that their interlocutor's responses would be agent-generated. Results show that an interactant is less likely to initiate repairs when an agent-interlocutor communicates via a text screen interface as well as when they explicitly know their interlocutor's words to be agent-generated. That is to say, people demonstrate the most “intersubjective effort” toward establishing common ground when they engage an agent under the same social psychological conditions as face-to-face human-human interaction (i.e., when they both encounter another human body and assume that they are speaking to an autonomously-communicating person). This article's methodology presents a novel means of benchmarking intersubjectivity and intersubjective effort in human-agent interaction

    Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

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    Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design
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