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Multiheaded deep learning chatbot for increasing production and marketing

- Zheng, Shiyong, Yahya, Zahrah, Wang, Lei, Zhang, Ruihang, Hoshyar, Azadeh

  • Authors: Zheng, Shiyong , Yahya, Zahrah , Wang, Lei , Zhang, Ruihang , Hoshyar, Azadeh
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Information Processing and Management Vol. 60, no. 5 (2023), p.
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  • Description: Some businesses on product development prefer to use a chatbot for judging the customer's view. Today, the ability of a chatbot to consider the context is challenging due to its technical nature. Sometimes, it may misjudge the context, making the wrong decision in predicting the product's originality in the market. This task of chatbot helps the enterprise make huge profits from accurate predictions. However, chatbots may commit errors in dialogs and bring inappropriate responses to users, reducing the confidentiality of product and marketing information. This, in turn, reduces the enterprise gain and imposes cost complications on businesses. To improve the performance of chatbots, AI models are used based on deep learning concepts. This research proposes a multi-headed deep neural network (MH-DNN) model for addressing the logical and fuzzy errors caused by retrieval chatbot models. This model cuts down on the error raised from the information loss. Our experiments extensively trained the model on a large Ubuntu dialog corpus. The recall evaluation scores showed that the MH-DNN approach slightly outperformed selected state-of-the-art retrieval-based chatbot approaches. The results obtained from the MHDNN augmentation approach were pretty impressive. In our proposed work, the MHDNN algorithm exhibited accuracy rates of 94% and 92%, respectively, with and without the help of the Seq2Seq technique. © 2023 Elsevier Ltd

Can intelligent agents improve data quality in online questionnaires? A pilot study

- Söderström, Arne, Shatte, Adrian, Fuller-Tyszkiewicz, Matthew

  • Authors: Söderström, Arne , Shatte, Adrian , Fuller-Tyszkiewicz, Matthew
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Behavior Research Methods Vol. 53, no. 5 (2021), p. 2238-2251
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  • Description: We explored the utility of chatbots for improving data quality arising from collection via sonline surveys. Three-hundred Australian adults sampled via Prolific Academic were randomized across chatbot-supported or unassisted online questionnaire conditions. The questionnaire comprised validated measures, along with challenge items formulated to be confusing yet aligned with the validated targets. The chatbot condition provided optional assistance with item clarity via a virtual support agent. Chatbot use and user satisfaction were measured through session logs and user feedback. Data quality was operationalized as between-group differences in relationships among validated and challenge measures. Findings broadly supported chatbot utility for online surveys, showing that most participants with chatbot access utilized it, found it helpful, and demonstrated modestly improved data quality (vs. controls). Absence of confusion for one challenge item is believed to have contributed to an underestimated effect. Findings show that assistive chatbots can enhance data quality, will be utilized by many participants if available, and are perceived as beneficial by most users. Scope constraints for this pilot study are believed to have led to underestimated effects. Future testing with longer-form questionnaires incorporating expanded item difficulty may further understanding of chatbot utility for survey completion and data quality. © 2021, The Psychonomic Society, Inc.
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