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  1. بهبود تولید پاسخ در پرسش و پاسخ مکالمه‌ ای با کمک یادگیری ژرف

Improve Answer Generation in Conversational Questions and Answering with Deep Learning

Alavian, Hesam | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57434 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Sameti, Hossein
  7. Abstract:
  8. Question and answer systems offer a means to acquire information in various formats. One such method is conversational question and answer, where the system possesses a text containing the information and, through a series of exchanges, provides the user with the needed information. In most systems, the responses generated by the system are either single-word or very brief, which is considered a weakness. Recent research endeavors have employed pre-trained language models to bring the system-generated responses closer to natural language. In a recent study, a system receives a set of questions along with short answers, then produces complete and fluent responses. This method utilizes parsers and syntactic transformers to aggregate data for response generation and selects the best answer based on a BERT model classification. However, a drawback of this method is that it takes a response span along with the question and transforms it into a coherent and complete answer, thus losing efficiency if this response span is unavailable. In this research, neural networks, pre-trained language models, information retrieval methods, and processing semantic relationships existing within texts are employed to generate accurate, complete, and fluent responses in a conversation. Building upon necessary background studies and prior works, a multi-stage system called docalog was designed and trained using RoBERTa and LaBSE language models on the multidoc2dial dataset, which was presented at a conference of the same name in 2022, achieving promising results in various aspects. Subsequently, in this study, I continued my research on improving the generation of complete and fluent responses, achieving a 20\% improvement in F1 score for complete responses using the BART language model compared to the baseline model. Continuing my research journey in this thesis, conducted in the English language, I furthered my investigations in the Persian language. One of the achievements of this research is the creation of the first conversational question and answer dataset in Persian, comprising over 4000 questions and answers. Training and fine-tuning various models specific to the Persian language on this dataset yielded results, which will also be presented in this section
  9. Keywords:
  10. Natural Language Understanding (NLU) ; Conversational Question Answering (CQA) ; Deep Learning ; Language Models Combination ; Information Retrieval

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