Closed Domain Question Answering Research Articles

CLOSED-DOMAIN QUESTION-ANSWERING SYSTEM FOR INDIAN LEGAL ACTS

: In Today’s digital age, access to legal knowledge among students is crucial for fostering a well-informed citizenry. However, comprehending the intricacies of provisions of Indian Legal Acts, especially in a complex legal system like India’s, can be daunting, even for seasoned professionals, let alone for educational purposes among children. To bridge this gap and foster legal literacy from a young age, we introduced a PDF-based Closed-domain Question-Answering (CDQA) System for Indian Legal Acts. Our system simplifies educating students about provisions of Indian Legal Acts such as RTI(Right to Education), RTE(Right to Education), Anti-Dowry Act, etc. By leveraging natural language processing(NLP) techniques and machine learning algorithms, our system, powered by LangChain, enables users to pose questions in natural language related to Indian Legal Acts. LangChain represents a groundbreaking advancement in question-answer systems, harnessing the power of cutting-edge language models to provide accurate and comprehensive responses to user queries. Developed based on state-of-the-art NLP techniques, LangChain is a versatile and highly adaptable tool that offers a versatile solution with transformative potential across diverse industries, including education, healthcare, legal research, customer support, etc.

An Implementation of Voice Assistant for Hospitality

Voice user interface has gained popularity in the recent years. A chatbot is a machine with the ability to answer automatically through a conversational interface. Instead of using mouse and keyboards as input and screen as output, a chatbot with extra voice user interface feature improve the system and enhance the user experience. A chatbot is considered as one of the most exceptional and promising expressions of human computer interaction. Voice-based chatbots or artificial intelligence (AI) devices transform humancomputer bidirectional interactions that allow users to navigate an interactive voice response (IVR) system with their voice generally using natural language. In this paper, we focus on voice based chatbots for mediating interactions between hotels and guests from both the hospitality technology providers’ and guests’ perspectives. A hotel web application with voice user interface was implemented which provides voice input/output interface to enhance the user experience. Speech recognition component was used to dictate the user voice input to text. Speech synthesis API was used for text to voice conversion. A closed domain question answering (cdQA) Natural Language Processing (NLP) solution was used for processingof query and return the best answer possible.

Transfer Learning for Closed Domain Question Answering in COVID-19

COVID-19 has been a popular issue around 2019 until today. Recently, there has been a lot of research being conducted to utilize a big amount of data discussing about COVID-19. In this work, we conduct a closed domain question answering (CDQA) task in COVID-19 using transfer learning technique. The transfer learning technique is adopted because a large benchmark for question answering about COVID-19 is still unavailable. Therefore, rich knowledge learned from a large benchmark of open domain QA are utilized using transfer learning to improve the performance of our CDQA system. We use retriever-reader framework for our CDQA system, and propose to use Sequential Dependence Model (SDM) as our retriever component to enhance the effectiveness of the system. Our result shows that the use of SDM retriever can improve the F-1 score of the state-of-the-art baseline CDQA system using BM25 and TF-IDF+cosine similarity retriever by 3,26% and 32,62%, respectively. The optimal parameter settings for our CDQA system are found to be as follows: using 20 top-ranked documents as the retriever’s output, five sentences as the passage length, and BERT-Large-Uncased model as the reader. In this optimal parameter setting, SDM retriever can improve the F-1 score of the state-of-the-art baseline CDQA system using BM25 by 5,06 % and TF-IDF+cosine similarity retriever by 24,94 %. Our last experiment then confirms the merit of using transfer learning, since our best-performing model (double fine-tune SQuAD and COVID-QA) is shown to gain eight times higher accuracy than the baseline method without using transfer learning. Further fine-tuning the transfer learning model using closed domain dataset (COVID-QA) can increase the accuracy of the transfer learning model that only fine-tuning with SQuAD by 27, 26%.

A relational tsetlin machine with applications to natural language understanding

Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is relational and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 × more compact KBs, along with an increase in answering accuracy from 94.83% to 99.48%. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding

Student Suite+, A Closed Domain Question Answering System for Educational Domain

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI). It is a study of how machines understand the language of humans. It aims to build systems that can understand text and perform tasks like language translation and classifying a topic. NLP has many applications. NLP tools help to process unstructured data. Question Answering (QA) system or information retrieval system falls under the category of NLP. The QA system mainly involves two methods, question analysis and answer extraction. Intelligent QA systems have always outperformed the non-intelligent QA systems. It is a neural network that is incorporated with the QA system to make it intelligent. Neural networks(NN) are a series of algorithms that are used to mimic the operations of a human brain to understand the relationships between data. NN can learn by themselves and produce the output that is not limited to the input provided to them.

Keywords based Closed Domain Question Answering System for Indian Penal Code Sections and Indian Amendment Laws

In information retrieval, Question Answering (QA) is the task of answering a question posed in natural language (NL) using either a pre-structured database or a collection of natural language documents without human intervention. Question Answering systems are categorized on their available resource for answers. The domain specific Question Answering System gives more exact and correct answers than web based Question Answering system as it is limited for only one domain resource to answer. This paper proposes the closed domain Question Answering System for handling the legal documents of Indian Penal Code (IPC) sections and Indian Amendment Laws to retrieve more precise answers. This system tries to retrieve the exact answers from stored knowledge-base for the query related to Indian Penal Code (IPC) sections and Indian Amendment Laws asked by user. This Keyword based Question Answering System works on structured, unstructured and non-question form queries. The closed domain Question Answering system gives more accurate answer than other open domain system as it restricted single resource. Keywords from both queries and answer corpus play important role for extracting answer.