Date of Publication

8-5-2023

Document Type

Master's Thesis

Degree Name

Master of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Thesis Advisor

Edwin Sybingco

Defense Panel Chair

Argel Bandala

Defense Panel Member

Ryan Vicerra
Anthony Jose

Abstract/Summary

The use of conversational agents can be extremely beneficial in many areas such as government offices, schools, banks, malls, etc. where people often make inquiries and responses from personnel can take some time. Many of these areas, however, have inquiries that involve domain-specific vocabulary and most likely do not have a large amount of data or computational resources to properly train a complex natural language processing (NLP) model. This paper proposes a method for creating a domain-specific virtual assistant using Generative Pre-Trained Transformer-3 (GPT-3) to generate paraphrases on a relatively small dataset, and a Sentence Transformer (SBERT) model with a distilled version of BERT (DistilBERT) base, pretrained on the Quora Question Pairs dataset, and fine-tuned on the augmented dataset. This method of creating a model is evaluated on the MS MARCO, SemEval, and PubMed datasets using mean average precision (MAP), precision at k (P@k), normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR) as performance metrics. The method was also demonstrated using a small dataset of 188 frequently asked questions from the De La Salle University website that also includes domain-specific vocabulary. The implementation of the fine-tuned model was demonstrated on a simple webpage and the results were found to be satisfactory.

Abstract Format

html

Language

English

Format

Electronic

Keywords

Chatbots; Natural language processing (Computer science); Human-computer interaction

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Embargo Period

8-12-2024

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Available for download on Monday, August 12, 2024

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