Example-based dialog modeling for practical multi-domain dialog system

Lee, Cheongjae; Jung, Sangkeun; Kim, Seokhwan; Lee, Gary Geunbae

doi:10.1016/j.specom.2009.01.008

Cited by 126 publications

(73 citation statements)

References 32 publications

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“…The example-based dialog model is the basis and model followed by the Interbot: Resume-based employment hiring dialog system. It was introduced by Lee et.al [7] in their study -Example-based dialog modeling for practical multi-domain dialog system‖ for deploying data-driven dialog systems. Its main idea is that a dialog manager uses dialog examples that are semantically indexed to a database, instead of domain-specific rules or probabilistic models for dialog management.The example-based dialog model methodology by Lee et al, presents a goal-oriented dialog system in a single domain as it explores a generic dialog-modeling framework for managing multi-domain goal-oriented dialogs and chat dialogs in the same framework.…”

Section: About the Interbot Systemmentioning

confidence: 99%

Interbot: A Credential Verification Chatbot Using an Enhanced Example Based Dialog Model

Aquino¹,

Bayona²,

Gonzales³

et al. 2014

IJFCC

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Abstract-Traditional resume based recruitment interviews and in-person interviews only allow companies to handle only a limited number of job applicants at a time. As a result, a substantial amount of time and money is misdirected on interviewing unqualified job applicants. The proponents developed a resume based employment interview chatbot, using an enhanced example based dialog model, to evaluate job applicants' consistency in their resume details and interview answers. The chatbot will replace the HR interviewer while maintaining the fundamental quality and naturalness of a resume based interview. The study aimed to improve the current hiring process, specifically the initial resume based interview conducted during job applicants' screening and also utilized the potential of chatbots to further improve their simulation of intelligent conversations by having an in-depth analysis on the content and meaning of the user's input.Index Terms-Chatbots, dialog system, expert system, natural language processing.

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Section: About the Interbot Systemmentioning

confidence: 99%

Interbot: A Credential Verification Chatbot Using an Enhanced Example Based Dialog Model

Aquino¹,

Bayona²,

Gonzales³

et al. 2014

IJFCC

View full text Add to dashboard Cite

show abstract

“…A set of the semantic and discourse constraints is similar to a set of state variables to represents the dialog state. Details of how EBDM framework determines the next system action are summarized in Lee et al [5].…”

Section: Query Relaxation Problemmentioning

confidence: 99%

“…We here suggest a simple correlation-based algorithm for relaxing constraints on failed queries for an example-based dialog modeling (EBDM) framework [5]. In the EBDM framework, a query (e.g., a set of semantic and discourse constraints) returns semantically-similar dialog examples to predict the next system action.…”

Section: Introductionmentioning

confidence: 99%

“…If constraints on the query are too strict, it may fail to produce an answer. Constraints on queries that fail to produce an answer (failed queries) are relaxed to satisfy more tuples so that queries can return relevant answers [1][2][3][4].We here suggest a simple correlation-based algorithm for relaxing constraints on failed queries for an example-based dialog modeling (EBDM) framework [5]. In the EBDM framework, a query (e.g., a set of semantic and discourse constraints) returns semantically-similar dialog examples to predict the next system action.…”

mentioning

confidence: 99%

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Correlation-based query relaxation for example-based dialog modeling

Lee

Jung

et al. 2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

Self Cite

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Abstract-Query relaxation refers to the process of reducing the number of constraints on a query if it returns no result when searching a database. This is an important process to enable extraction of an appropriate number of query results because queries that are too strictly constrained may return no result, whereas queries that are too loosely constrained may return too many results. This paper proposes an automated method of correlation-based query relaxation (CBQR) to select an appropriate constraint subset. The example-based dialog modeling framework was used to validate our algorithm. Preliminary results show that the proposed method facilitates the automation of query relaxation. We believe that the CBQR algorithm effectively relaxes constraints on failed queries to return more dialog examples. I. INTRODUCTIONA query consists of a set of attribute-value pairs in a relational database. These pairs are carefully chosen to include only a specified subset of the database. If constraints on the query are too strict, it may fail to produce an answer. Constraints on queries that fail to produce an answer (failed queries) are relaxed to satisfy more tuples so that queries can return relevant answers [1][2][3][4].We here suggest a simple correlation-based algorithm for relaxing constraints on failed queries for an example-based dialog modeling (EBDM) framework [5]. In the EBDM framework, a query (e.g., a set of semantic and discourse constraints) returns semantically-similar dialog examples to predict the next system action. However, failed queries are inevitable because of the data sparseness problem or because of prior errors by the speech recognition and language understanding modules. To avoid this problem, query constraints are relaxed using heuristic relaxation strategies so that more matches are found. However, these strategies cannot cover all situations perfectly because the strategies are prepared manually by human experts and it is difficult to know which query constraints should be relaxed in a specific situation. Therefore, this paper proposes a correlation-based query relaxation (CBQR) algorithm that uses correlation coefficients between pairs of query constraints to automatically select a constraint subset by removing irrelevant constraints.

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“…Currently, most EBDM systems rely on either canned responses by providing error messages [8] or templates for generation which may result a completely incomprehensible response [9]. On the other hand, SMT has been successfully used to address various NLP tasks [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Human-to-Human Conversation Examples for a Multi Domain Chat-Oriented Dialog System

Nio

Sakti

Neubig

et al. 2014

IEICE Trans. Inf. & Syst.

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Lasguido NIO†a) , Nonmember, Sakriani SAKTI †b) , Member, Graham NEUBIG †c) , Nonmember, Tomoki TODA †d) , and Satoshi NAKAMURA †e) , Members SUMMARY This paper describes the design and evaluation of a method for developing a chat-oriented dialog system by utilizing real human-to-human conversation examples from movie scripts and Twitter conversations. The aim of the proposed method is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) constructing an appropriate dialog corpora from raw movie scripts and Twitter data, and (2) developing an multi domain chat-oriented dialog management system which can retrieve a proper system response based on the current user query. To build a dialog corpus, we propose a unit of conversation called a tri-turn (a trigram conversation turn), as well as extraction and semantic similarity analysis techniques to help ensure that the content extracted from raw movie/drama script files forms appropriate dialog-pair (query-response) examples. The constructed dialog corpora are then utilized in a data-driven dialog management system. Here, various approaches are investigated including example-based (EBDM) and response generation using phrase-based statistical machine translation (SMT). In particular, we use two EBDM: syntactic-semantic similarity retrieval and TF-IDF based cosine similarity retrieval. Experiments are conducted to compare and contrast EBDM and SMT approaches in building a chat-oriented dialog system, and we investigate a combined method that addresses the advantages and disadvantages of both approaches. System performance was evaluated based on objective metrics (semantic similarity and cosine similarity) and human subjective evaluation from a small user study. Experimental results show that the proposed filtering approach effectively improve the performance. Furthermore, the results also show that by combing both EBDM and SMT approaches, we could overcome the shortcomings of each. key words: dialog corpora, response generation, example-based dialog modeling, semantic similarity, cosine similarity, machine translation IntroductionThe continuous growth of information technology is having an increasingly large impact on many aspects of our daily lives. The issue of communication via speech between human beings and information-processing machines is also becoming more important [1]. A common dream is to realize a technology that allows humans to communicate or have dialogs with machines through natural and spontaneous speech. ). Dialog systems can also be described by the amount of human intervention used in their construction, ranging from entirely hand-made to completely data-driven. Seminal work often limited interactions to a specific scenario (e.g. a Rogerian psychotherapist [4]) or were based on complex, knowledgerich rule-based systems for generating responses, which requi...

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Example-based dialog modeling for practical multi-domain dialog system

Cited by 126 publications

References 32 publications

Interbot: A Credential Verification Chatbot Using an Enhanced Example Based Dialog Model

Interbot: A Credential Verification Chatbot Using an Enhanced Example Based Dialog Model

Correlation-based query relaxation for example-based dialog modeling

Utilizing Human-to-Human Conversation Examples for a Multi Domain Chat-Oriented Dialog System

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