27 research outputs found

    Finding common ground: towards a surface realisation shared task

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    In many areas of NLP reuse of utility tools such as parsers and POS taggers is now common, but this is still rare in NLG. The subfield of surface realisation has perhaps come closest, but at present we still lack a basis on which different surface realisers could be compared, chiefly because of the wide variety of different input representations used by different realisers. This paper outlines an idea for a shared task in surface realisation, where inputs are provided in a common-ground representation formalism which participants map to the types of input required by their system. These inputs are derived from existing annotated corpora developed for language analysis (parsing etc.). Outputs (realisations) are evaluated by automatic comparison against the human-authored text in the corpora as well as by human assessors

    High efficiency realization for a wide-coverage unification grammar

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    We give a detailed account of an algorithm for efficient tactical generation from underspecified logical-form semantics, using a wide-coverage grammar and a corpus of real-world target utterances. Some earlier claims about chart realization are critically reviewed and corrected in the light of a series of practical experiments. As well as a set of algorithmic refinements, we present two novel techniques: the integration of subsumption-based local ambiguity factoring, and a procedure to selectively unpack the generation forest according to a probability distribution given by a conditional, discriminative model

    Using same-language machine translation to create alternative target sequences for text-to-speech synthesis

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    Modern speech synthesis systems attempt to produce speech utterances from an open domain of words. In some situations, the synthesiser will not have the appropriate units to pronounce some words or phrases accurately but it still must attempt to pronounce them. This paper presents a hybrid machine translation and unit selection speech synthesis system. The machine translation system was trained with English as the source and target language. Rather than the synthesiser only saying the input text as would happen in conventional synthesis systems, the synthesiser may say an alternative utterance with the same meaning. This method allows the synthesiser to overcome the problem of insufficient units in runtime

    Three reasons to adopt TAG-based surface realisation

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    Surface realisation from flat semantic formulae is known to be exponential in the length of the input. In this paper, we argue that TAG naturally supports the integration of three main ways of reducing complexity: polarity filtering, delayed adjunction and empty semantic items elimination. We support these claims by presenting some preliminary results of the TAG-based surface realiser

    Generating Tailored, Comparative Descriptions with Contextually Appropriate Intonation

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    Generating responses that take user preferences into account requires adaptation at all levels of the generation process. This article describes a multi-level approach to presenting user-tailored information in spoken dialogues which brings together for the first time multi-attribute decision models, strategic content planning, surface realization that incorporates prosody prediction, and unit selection synthesis that takes the resulting prosodic structure into account. The system selects the most important options to mention and the attributes that are most relevant to choosing between them, based on the user model. Multiple options are selected when each offers a compelling trade-off. To convey these trade-offs, the system employs a novel presentation strategy which straightforwardly lends itself to the determination of information structure, as well as the contents of referring expressions. During surface realization, the prosodic structure is derived from the information structure using Combinatory Categorial Grammar in a way that allows phrase boundaries to be determined in a flexible, data-driven fashion. This approach to choosing pitch accents and edge tones is shown to yield prosodic structures with significantly higher acceptability than baseline prosody prediction models in an expert evaluation. These prosodic structures are then shown to enable perceptibly more natural synthesis using a unit selection voice that aims to produce the target tunes, in comparison to two baseline synthetic voices. An expert evaluation and f0 analysis confirm the superiority of the generator-driven intonation and its contribution to listeners' ratings

    A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar

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    International audienceSurface realisers divide into those used in generation (NLG geared realisers) and those mirroring the parsing process (Reversible realisers). While the first rely on grammars not easily usable for parsing, it is unclear how the second type of realisers could be parameterised to yield from among the set of possible paraphrases, the paraphrase appropriate to a given generation context. In this paper, we present a surface realiser which combines a reversible grammar (used for parsing and doing semantic construction) with a symbolic means of selecting paraphrases

    Probabilistic generation of weather forecast texts

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    GenI, un réalisateur basé sur une grammaire réversible

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    National audienceEn génération, un réalisateur de surface a pour fonction de produire, à partir d'une représentation conceptuelle donnée, une phrase grammaticale. Les réalisateur existants soit utilisent une grammaire réversible et des méthodes statistiques pour déterminer parmi l'ensemble des sorties produites la plus plausible; soit utilisent des grammaires spécialisées pour la génération et des méthodes symboliques pour déterminer la paraphrase la plus appropriée à un contexte de génération donné. Dans cet article, nous présentons GenI, un réalisateur de surface basé sur une grammaire d'arbres adjoints pour le français qui réconcilie les deux approches en combinant une grammaire réversible avec une sélection symbolique des paraphrases

    Recovering From Errors in Conversational Dialogue Systems

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    Spoken dialogue systems can encounter different types of errors, including non-understanding errors. This is where the system realises the user has spoken, but does not understand their utterance. Strategies for dealing with this kind of error have been proposed and tested in the context of slot-filling systems, for example by Dan Bohus with a system which helps reserve conference rooms [1]. However there has been little work into possible strategies for more conversational settings. This dissertation looks at how we could recover from non-understanding errors experienced by a robot tourguide, and tests the strategies in an experimental study. The main hypothesis of this study is that it is beneficial to use strategies which are designed to do something smarter than just asking the user to repeat themselves. The strategies implemented are motivated by the findings of work done on task-based dialogue systems [1,2,3], which suggest it is useful to move the user on through the dialogue instead of getting caught up with the non-understanding error
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