Explaining Queries over Web Tables to Non-Experts

Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the results. Errors in the translation process are not uncommon, and users typically struggle to understand whether their query has been mapped correctly. We address this problem by explaining the obtained formal queries to non-expert users. Two methods for query explanations are presented: the first translates queries into NL, while the second method provides a graphic representation of the query cell-based provenance (in its execution on a given table). Our solution augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase. Experiments, including a user study conducted on Amazon Mechanical Turk, show our solution to improve both the correctness and reliability of an NL interface.Comment: Short paper version to appear in ICDE 201

An investigation into teaching description and retrieval for constructed languages : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

The research presented in this thesis focuses on an investigation on teaching concepts for constructed languages, and the development of a teaching tool, called VISL, for teaching a specific constructed language. Constructed languages have been developed for integration with computer systems to overcome ambiguities and complexities existing in natural language in information description and retrieval. Understanding and using properly these languages is one of the keys for successful use of these computer systems Unfortunately, current teaching approaches are not suitable for users to learn features of those languages easily. There are different types of constructed languages. Each has specific features adapted for specific uses but they have in common explicitly constructed grammar. In addition, a constructed language commonly embeds a powerful query engine that makes it easy for computer systems to search for correct information from descriptions following the conditions of the queries. This suggests new teaching principles that should be easily adaptable to teach any specific structured language's structures and its specific query engine. In this research, teaching concepts were developed that offer a multi-modal approach to teach constructed languages and their specific query engines. These concepts are developed based on the efficiencies of language structure diagrams over the cumbersome and non-transparent nature of textual explanations, and advantages of active learning strategies in enhancing language understanding. These teaching concepts then were applied successfully for a constructed language, FSCL, as an example The research also explains howr the concepts developed can be adapted for other constructed languages. Based on the developed concepts, a Computer Aided Language Learning (CALL) application called VISL is built to teach FSCL. The application is integrated as an extension module in PAC, the computer system using FSCL for description and retrieval of information in qualitative analysis. In this application, users will learn FSCL through an interconnection of four modes: FSCL structures through the first two modes and its specific query engine through the sccond two modes After going through four modes, users will have developed full understanding for the language. This will help users to construct a consistent vocabulary database, produce descriptive sentences conducive to retrieval, and create appropriate query sentences for obtaining relevant search results

SWAN: An expert system with natural language interface for tactical air capability assessment

SWAN is an expert system and natural language interface for assessing the war fighting capability of Air Force units in Europe. The expert system is an object oriented knowledge based simulation with an alternate worlds facility for performing what-if excursions. Responses from the system take the form of generated text, tables, or graphs. The natural language interface is an expert system in its own right, with a knowledge base and rules which understand how to access external databases, models, or expert systems. The distinguishing feature of the Air Force expert system is its use of meta-knowledge to generate explanations in the frame and procedure based environment

Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.Comment: Accepted at ACL 2020 as a long conference pape