What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
@inproceedings{Jansen2016WhatsIA, title={What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams}, author={Peter Alexander Jansen and Niranjan Balasubramanian and Mihai Surdeanu and Peter Clark}, booktitle={International Conference on Computational Linguistics}, year={2016} }
QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements based on appropriate sources of evidence to be used for the QA task. We create requirements by first identifying suitable sentences in a knowledge base that support the correct answer, then use these…
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16 References
Exploring Markov Logic Networks for Question Answering
- 2015
Computer Science
EMNLP
A system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic, called Praline, which demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches.
Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions
- 2016
Computer Science
AAAI
This paper describes an alternative approach that operates at three levels of representation and reasoning: information retrieval, corpus statistics, and simple inference over a semi-automatically constructed knowledge base, to achieve substantially improved results.
A study of the knowledge base requirements for passing an elementary science test
- 2013
Computer Science
AKBC '13
The analysis suggests that as well as fact extraction from text and statistically driven rule extraction, three other styles of automatic knowledge base construction (AKBC) would be useful: acquiring definitional knowledge, direct 'reading' of rules from texts that state them, and, given a particular representational framework, acquisition of specific instances of those models from text.
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
- 2016
Computer Science
ICLR
This work argues for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering, and classify these tasks into skill sets so that researchers can identify (and then rectify) the failings of their systems.
Question Answering via Integer Programming over Semi-Structured Knowledge
- 2016
Computer Science
IJCAI
This work proposes a structured inference system for this task, formulated as an Integer Linear Program (ILP), that answers natural language questions using a semi-structured knowledge base derived from text, including questions requiring multi-step inference and a combination of multiple facts.
Learning question classifiers: the role of semantic information
- 2005
Computer Science
Natural Language Engineering
It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy.
Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
- 2015
Computer Science
TACL
This work introduces a higher-order formalism that allows all these lexical semantic models to chain direct evidence to construct indirect associations between question and answer texts, by casting the task as the traversal of graphs that encode direct term associations.
A library of generic concepts for composing knowledge bases
- 2001
Computer Science
K-CAP '01
The component library is described, a hierarchy of reusable, composable, domain-independent knowledge units, influenced heavily by linguistic resources, which emphasizes coverage, access, access and semantics.
My Computer Is an Honor Student - but How Intelligent Is It? Standardized Tests as a Measure of AI
- 2016
Computer Science
AI Mag.
It is argued that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling - critical skills for any machine that lays claim to intelligence.
Discourse Complements Lexical Semantics for Non-factoid Answer Reranking
- 2014
Computer Science
ACL
We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation…