scholarly journals QED: A Framework and Dataset for Explanations in Question Answering

2021 ◽  
Vol 9 ◽  
pp. 790-806
Author(s):  
Matthew Lamm ◽  
Jennimaria Palomaki ◽  
Chris Alberti ◽  
Daniel Andor ◽  
Eunsol Choi ◽  
...  
Keyword(s):  
Formal Language ◽  
Question Answering ◽  
User Study ◽  
Promising Result ◽  
Formal Semantic ◽  
Question Answering System ◽  
Post Hoc ◽  
The Relationship ◽  
Explanation Generation ◽  
Large User

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

scholarly journals Introducing MathQA: a Math-Aware question answering system

2018 ◽  
Vol 46 (4) ◽  
pp. 214-224 ◽  
Author(s):  
Moritz Schubotz ◽  
Philipp Scharpf ◽  
Kaushal Dudhat ◽  
Yash Nagar ◽  
Felix Hamborg ◽  
...  
Keyword(s):  
Mathematical Knowledge ◽  
Design Methodology ◽  
Question Answering ◽  
User Study ◽  
Content Type ◽  
Question Answering System ◽  
Knowledge Based ◽  
Natural Language Question ◽  
Language Question

Purpose This paper aims to present an open source math-aware Question Answering System based on Ask Platypus. Design/methodology/approach The system returns as a single mathematical formula for a natural language question in English or Hindi. These formulae originate from the knowledge-based Wikidata. The authors translate these formulae to computable data by integrating the calculation engine sympy into the system. This way, users can enter numeric values for the variables occurring in the formula. Moreover, the system loads numeric values for constants occurring in the formula from Wikidata. Findings In a user study, this system outperformed a commercial computational mathematical knowledge engine by 13 per cent. However, the performance of this system heavily depends on the size and quality of the formula data available in Wikidata. As only a few items in Wikidata contained formulae when the project started, the authors facilitated the import process by suggesting formula edits to Wikidata editors. With the simple heuristic that the first formula is significant for the paper, 80 per cent of the suggestions were correct. Originality/value This research was presented at the JCDL17 KDD workshop.

Study on Finding Experts in Community Question-Answering System

2014 ◽  
Vol 513-517 ◽  
pp. 1760-1764 ◽  
Author(s):  
Rong Rong Yang ◽  
Jian Hua Wu
Keyword(s):  
Web 2.0 ◽  
Web Mining ◽  
Question Answering ◽  
Service Model ◽  
Question Answering System ◽  
Community Question Answering ◽  
Questions And Answers ◽  
Hits Algorithm ◽  
The Relationship ◽  
The Web

As one of the new service model of Web 2.0, the emergence of Community Question-Answering system brings a new way for users to obtain information. However, the explosive growth of users and information, it will be hard for users to obtain the information quickly and accurately. Therefore, it is important to find experts in Community Question-Answering system to improve the accuracy and efficiency of information obtaining. This paper firstly analyzed the relationship among users, questions, and answers in Community Question-Answering system, and built the user diagram, and then by means of the Web mining technology, that is the link analysis weighted HITS algorithm, to find experts out. Finally, three evaluation indices were used to measure the validity of the experts finding algorithm. Experimental results show the effectiveness of the weighted HITS algorithm.

scholarly journals Nonreligious Employees’ Perceptions of Microaggressions and Their Relationship With Job Satisfaction as Moderated by Calling

2021 ◽  
pp. 089484532110133
Author(s):  
Jessica N. Schultz ◽  
Melanie E. Leuty ◽  
Emily Bullock-Yowell ◽  
Richard Mohn
Keyword(s):  
Job Satisfaction ◽  
Vocational Guidance ◽  
Person Organization Fit ◽  
Post Hoc ◽  
The Relationship ◽  
The U.S

Workplace microaggressions are related to person–organization fit (P-O fit) and job satisfaction. Additionally, P-O fit and calling predict job satisfaction. Given the religious connotations of calling, research has excluded study of these relationships in nonreligious samples, a growing segment of the U.S. population. To address this, it was predicted that P-O fit would mediate the relationship between microaggressions and job satisfaction, and calling would moderate the relationship between microaggressions and P-O fit. In a sample of 296 nonreligious employed adults, microaggressions predicted job satisfaction, while calling predicted P-O fit and job satisfaction; however, P-O fit did not mediate these relationships, and calling did not moderate microaggressions and P-O fit. Post hoc analyses revealed that calling moderated microaggressions and job satisfaction. Implications for research and vocational guidance with nonreligious individuals are discussed.

scholarly journals Metacognitive control processes in question answering: help seeking and withholding answers

2021 ◽  
Author(s):  
Monika Undorf ◽  
Iris Livneh ◽  
Rakefet Ackerman
Keyword(s):  
Help Seeking ◽  
Question Answering ◽  
Monetary Incentives ◽  
Metacognitive Control ◽  
Self Regulated Learning ◽  
Response Strategies ◽  
Seeking Help ◽  
Subjective Confidence ◽  
Different Populations ◽  
The Relationship

AbstractWhen responding to knowledge questions, people monitor their confidence in the knowledge they retrieve from memory and strategically regulate their responses so as to provide answers that are both correct and informative. The current study investigated the association between subjective confidence and the use of two response strategies: seeking help and withholding answers by responding “I don’t know”. Seeking help has been extensively studied as a resource management strategy in self-regulated learning, but has been largely neglected in metacognition research. In contrast, withholding answers has received less attention in educational studies than in metacognition research. Across three experiments, we compared the relationship between subjective confidence and strategy use in conditions where participants could choose between submitting answers and seeking help, between submitting and withholding answers, or between submitting answers, seeking help, and withholding answers. Results consistently showed that the association between confidence and help seeking was weaker than that between confidence and withholding answers. This difference was found for participants from two different populations, remained when participants received monetary incentives for accurate answers, and replicated across two forms of help. Our findings suggest that seeking help is guided by a wider variety of considerations than withholding answers, with some considerations going beyond improving the immediate accuracy of one’s answers. We discuss implications for research on metacognition and regarding question answering in educational and other contexts.

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