NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation

Chang, Jinxin; He, Ran; Xu, Hui; Han, Kun‐Yeun; Wang, Longbiao; Li, Xiangang; Dang, Jianwu

doi:10.1109/icdm.2019.00015

Cited by 2 publications

(1 citation statement)

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“…Correctness instead evaluates the quality of the output provided by the chatbot measured in terms of the correct answers provided to the users [62], and the accuracy of the provided content. Proactiveness (in some sources referred to as Control of topic transfer [63], Initiate a new topic appropriately [46], Topic Switching [64] and Intent to Interact [65]), is the capability of the chatbot to switch or initiate new topics autonomously.…”

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confidence: 99%

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

Coppola

Ardito

2021

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The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products.

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Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

Coppola

Ardito

2021

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Exploiting Explicit and Inferred Implicit Personas for Multi-turn Dialogue Generation

Wang

et al. 2021

Natural Language Processing and Chinese Computing

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NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation

Cited by 2 publications

References 22 publications

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

Exploiting Explicit and Inferred Implicit Personas for Multi-turn Dialogue Generation

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