Deep Auto Encoder based Chatbot for Discrete Math Course

Ullah, Sibghat; Liaqat, Momina; Asif, Aqsa; Khan, Asifullah; Aslam, Umair; Asif, Hifsa

doi:10.1109/raeecs56511.2022.9954572

Cited by 3 publications

(2 citation statements)

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“…The model used to perform encoding on the training data is the Bidirectional Encoder Representations from Transformer (BERT) Base or 'bertbase-nli-mean-tokens'. The selected BERT is a type that can perform bidirectional language processing and obtain representations of the entire text (Ullah et al, 2022). Bert-base-nli-mean-tokens is a BERT-based model that pools mean-tokens to generate sentence embeddings and has achieved a 77.12 performance on the STS benchmark dataset, making it suitable for various NLP tasks due to its ability to capture complex relationships between words and sentences (Conneau et al, 2017).…”

Section: Encodingmentioning

confidence: 99%

“…Employee retention is an important strategy for HR managers (Samuel & Chipunza, 2009). There is much evidence that employee retention is important for managers to address the increasing levels and costs of employee turnover (Aman- Ullah et al, 2022;Zayed et al, 2022). For example, factors of influence employee retention, including wage, payment length, employment agreement, labor market mobility, and participation in significant building projects (Le et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Learning Pathway in Human Capital Development Programs with BERT Transformer

Zidny,

Puspitasari,

Yuadi

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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This study presents a novel approach to assist learning analysts in identifying suitable learning pathways based on historical training data through the utilization of text mining techniques. The dataset utilized in this research comprises training data from the year 2021 and the Course Development Management Program (CDMP) catalogue. The BERT 'bert-base-nli-mean-tokens' model is employed for encoding purposes. By comparing the training data names from 2021 with the CDMP catalogue using cosine similarity and dot score, valuable insights are obtained. The findings indicate that cosine similarity is a more effective measure for interpreting the data, thereby simplifying the process for learning analysts and managers in identifying appropriate learning paths for their employees. This research provides a practical solution that leverages text mining techniques to optimize the analysis and decision-making processes in learning and development domains, enabling organizations to enhance the effectiveness and efficiency of their training programs.

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Section: Encodingmentioning

confidence: 99%