Original/Review Paper
H.3.8. Natural Language Processing
Nura Esfandiari; Kourosh Kiani; Razieh Rastgoo
Abstract
A chatbot is a computer program system designed to simulate human-like conversations and interact with users. It is a form of conversational agent that utilizes Natural Language Processing (NLP) and sequential models to understand user input, interpret their intent, and generate appropriate answer. This ...
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A chatbot is a computer program system designed to simulate human-like conversations and interact with users. It is a form of conversational agent that utilizes Natural Language Processing (NLP) and sequential models to understand user input, interpret their intent, and generate appropriate answer. This approach aims to generate word sequences in the form of coherent phrases. A notable challenge associated with previous models lies in their sequential training process, which can result in less accurate outcomes. To address this limitation, a novel generative chatbot is proposed, integrating the power of Reinforcement Learning (RL) and transformer models. The proposed chatbot aims to overcome the challenges associated with sequential training by combining these two approaches. The proposed approach employs a Double Deep Q-Network (DDQN) architecture with utilizing a transformer model as the agent. This agent takes the human question as an input state and generates the bot answer as an action. To the best of our knowledge, this is the first time that a generative chatbot is proposed using a DDQN architecture with the embedded transformer as an agent. Results on two public datasets, Daily Dialog and Chit-Chat, validate the superiority of the proposed approach over state-of-the-art models involves employing various evaluation metrics.
Original/Review Paper
F.4.18. Time series analysis
Fatemeh Moodi; Amir Jahangard Rafsanjani; Sajjad Zarifzadeh; Mohammad Ali Zare Chahooki
Abstract
This article proposes a novel hybrid network integrating three distinct architectures -CNN, GRU, and LSTM- to predict stock price movements. Here with Combining Feature Extraction and Sequence Learning and Complementary Strengths can Improved Predictive Performance. CNNs can effectively identify short-term ...
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This article proposes a novel hybrid network integrating three distinct architectures -CNN, GRU, and LSTM- to predict stock price movements. Here with Combining Feature Extraction and Sequence Learning and Complementary Strengths can Improved Predictive Performance. CNNs can effectively identify short-term dependencies and relevant features in time series, such as trends or spikes in stock prices. GRUs designed to handle sequential data. They are particularly useful for capturing dependencies over time while being computationally less expensive than LSTMs. In the hybrid model, GRUs help maintain relevant historical information in the sequence without suffering from vanishing gradient problems, making them more efficient for long sequences. LSTMs excel at learning long-term dependencies in sequential data, thanks to their memory cell structure. By retaining information over longer periods, LSTMs in the hybrid model ensure that important trends over time are not lost, providing a deeper understanding of the time series data. The novelty of the 1D-CNN-GRU-LSTM hybrid model lies in its ability to simultaneously capture short-term patterns and long-term dependencies in time series data, offering a more nuanced and accurate prediction of stock prices. The data set comprises technical indicators, sentiment analysis, and various aspects derived from pertinent tweets. Stock price movement is categorized into three categories: Rise, Fall, and Stable. Evaluation of this model on five years of transaction data demonstrates its capability to forecast stock price movements with an accuracy of 0.93717. The improvement of proposed hybrid model for stock movement prediction over existing models is 12% for accuracy and F1-score metrics.
Applied Article
H.3.8. Natural Language Processing
Davud Mohammadpur; Mehdi Nazari
Abstract
Text summarization has become one of the favorite subjects of researchers due to the rapid growth of contents. In title generation, a key aspect of text summarization, creating a concise and meaningful title is essential as it reflects the article's content, objectives, methodologies, and findings. Thus, ...
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Text summarization has become one of the favorite subjects of researchers due to the rapid growth of contents. In title generation, a key aspect of text summarization, creating a concise and meaningful title is essential as it reflects the article's content, objectives, methodologies, and findings. Thus, generating an effective title requires a thorough understanding of the article. Various methods have been proposed in text summarization to automatically generate titles, utilizing machine learning and deep learning techniques to improve results. This study aims to develop a title generation system for scientific articles using transformer-based methods to create suitable titles from article abstracts. Pre-trained transformer-based models like BERT, T5, and PEGASUS are optimized for constructing complete sentences, but their ability to generate scientific titles is limited. We have attempted to improve this limitation by presenting a proposed method that combines different models along with a suitable dataset for training. To create our desired dataset, we collected abstracts and titles of articles published on the ScienceDirect.com website. After performing preprocessing on this data, we developed a suitable dataset consisting of 50,000 articles. The results from the evaluations of the proposed method indicate more than 20% improvement based on various ROUGE metrics in the generation of scientific titles. Additionally, an examination of the results by experts in each scientific field revealed that the generated titles are also acceptable to these specialists.
