Training a sentence planner for spoken dialogue using boosting

Walker, Marilyn A.; Rambow, Owen; Rogati, Monica

doi:10.1016/s0885-2308(02)00027-x

Cited by 66 publications

(49 citation statements)

References 25 publications

(29 reference statements)

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“…There are many different ways we could learn such models [10,23,24]. Here, we estimate models using vectors of features representing individual characters, and then derive distinctive features for that character by normalizing these feature counts against a representative population.…”

Section: Learning Character Modelsmentioning

confidence: 99%

Perceived or Not Perceived: Film Character Models for Expressive NLG

Walker

Grant

Sawyer

et al. 2011

Interactive Storytelling

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Abstract. This paper presents a method for learning models of character linguistic style from a corpus of film dialogues and tests the method in a perceptual experiment. We apply our method in the context of SpyFeet, a prototype role playing game. In previous work, we used the PERSONAGE engine to produce restaurant recommendations that varied according to the speaker's personality [14,12]. Here we show for the first time that: (1) our expressive generation engine can operate on content from the story structures of an RPG; (2) PERSONAGE parameter models can be learned from film dialogue; (3) PERSONAGE rule-based models for extraversion and neuroticism are be perceived as intended in a new domain (SpyFeet character utterances); and (4) that the parameter models learned from film dialogue are generally perceived as being similar to the character that the model is based on. This is the first step of our long term goal to create off-theshelf tools to support authors in the creation of interesting dramatic characters and dialogue partners, for a broad range of types of interactive stories and role playing games.

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Section: Learning Character Modelsmentioning

confidence: 99%

Perceived or Not Perceived: Film Character Models for Expressive NLG

Walker

Grant

Sawyer

et al. 2011

Interactive Storytelling

Self Cite

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“…Ratnaparkhi proposed four trainable approaches of NLG and evaluated them [4]. The first one is the baseline.…”

Section: Natural Language Generationmentioning

confidence: 99%

“…proposed a trainable approach for NLG in the air travel domain [4]. And Oh and Rudnicky describe a statistical approach in the air travel domain [9].…”

Section: Natural Language Generationmentioning

confidence: 99%

“…In order to overcome the limitations, trainable approaches have been attempted recently [3,4,5,6]. Walker et al proposed the sentence plan tree representing the structure of a sentence while training is conducted by a RankBoost method [4].…”

Section: Introductionmentioning

confidence: 99%

“…Walker et al proposed the sentence plan tree representing the structure of a sentence while training is conducted by a RankBoost method [4]. A stochastic model generating sentences was presented by Levin et al while they have applied it to a dialogue-based travel planning system [5].…”

Section: Introductionmentioning

confidence: 99%

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Language Generation for Conversational Agent by Evolution of Plan Trees with Genetic Programming

Lim

Cho

2005

Modeling Decisions for Artificial Intelligence

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Abstract. As dialogue systems are widely demanded, the research on natural language generation in dialogue has raised interest. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and customized dialogues with humans. The paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method.

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A Corpus-Based Approach for Cooperative Response Generation in a Dialog System

Meng

Ning

et al. 2006

Chinese Spoken Language Processing

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Abstract. This paper presents a corpus-based approach for cooperative response generation in a spoken dialog system for the Hong Kong tourism domain. A corpus with 3874 requests and responses is collected using Wizard-ofOz framework. The corpus then undergoes a regularization process that simplifies the interactions to ease subsequent modeling. A semi-automatic process is developed to annotate each utterance in the dialog turns in terms of their key concepts (KC), task goal (TG) and dialog acts (DA). TG and DA characterize the informational goal and communicative goal of the utterance respectively. The annotation procedure is integrated with a dialog modeling heuristic and a discourse inheritance strategy to generate a semantic abstraction (SA), in the form of {TG, DA, KC}, for each user request and system response in the dialog. Semantic transitions, i.e. {TG, DA, KC} user →{TG, DA, KC} system , may hence be directly derived from the corpus as rules for response message planning. Related verbalization methods may also be derived from the corpus and used as templates for response message realization. All the rules and templates are stored externally in a human-readable text file which brings the advantage of easy extensibility of the system. Evaluation of this corpus based approach shows that 83% of the generated responses are coherent with the user's request and qualitative rating achieves a score of 4.0 on a five-point Likert scale.

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Training a sentence planner for spoken dialogue using boosting

Cited by 66 publications

References 25 publications

Perceived or Not Perceived: Film Character Models for Expressive NLG

Perceived or Not Perceived: Film Character Models for Expressive NLG

Language Generation for Conversational Agent by Evolution of Plan Trees with Genetic Programming

A Corpus-Based Approach for Cooperative Response Generation in a Dialog System

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