Matching Items (3)
Description
The need for automated / computational fact checking has grown substantially in recent times due to the high volume of false information and limited workforce of human fact checkers. This need has spawned research and new developments in this field and has created many different systems and approaches to this complex problem. This paper attempts to not just explain the most popular methods that are currently being used, but provide experimental results of the comparison of two different systems, the replication of results from their respective papers, and an annotated data-set of different test sentences to be used in these systems.
ContributorsRosenkilde, Trevor Curtis (Author) / Papotti, Paolo (Thesis director) / Candan, Kasim (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This project compiled findings from both primary and secondary applied research on the design and content structure on the current knowledge bases (KB) of product related documentation for the accesso Siriusware product suite. The findings from the research and study improved understanding surrounding our end-user perceptions of knowledge base functionality and usability. In addition, the findings became the framework for building an implementation strategy to improve knowledge base design and development. The implementation strategy is included in the report, and these findings will be used to update documentation and develop the KB.
ContributorsSwiontek, Amanda (Author) / Batova, Tatiana (Degree committee member) / Mara, Andrew (Degree committee member) / Brumberger, Eva (Degree committee member)
Created2017-11-29
Description
Visual question answering (VQA) is a task that answers the questions by giving an image, and thus involves both language and vision methods to solve, which make the VQA tasks a frontier interdisciplinary field. In recent years, as the great progress made in simple question tasks (e.g. object recognition), researchers start to shift their interests to the questions that require knowledge and reasoning. Knowledge-based VQA requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverages different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models' performance. To address this issue, this paper collects a natural language knowledge base that can be used for any question answering (QA) system. Moreover, a Visual Retriever-Reader pipeline is proposed to approach knowledge-based VQA, where the visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. The retriever is constructed with two versions: term based retriever which uses best matching 25 (BM25), and neural based retriever where the latest dense passage retriever (DPR) is introduced. To encode the visual information, the image and caption are encoded separately in the two kinds of neural based retriever: Image-DPR and Caption-DPR. There are also two styles of readers, classification reader and extraction reader. Both the retriever and reader are trained with weak supervision. The experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge.
ContributorsZeng, Yankai (Author) / Baral, Chitta (Thesis advisor) / Yang, Yezhou (Committee member) / Ghayekhloo, Samira (Committee member) / Arizona State University (Publisher)
Created2021