What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams

By Peter Alexander Jansen, Niranjan Balasubramanian, Mihai Surdeanu, Peter Clark. 2016

67 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering (2018, methodology): “For the remaining questions, we categorized the additional facts into five high-level categories (and collapsed the remaining facts into a catch-all OTHERS category) based on previous approaches on similar science questions (Clark et al., 2018; Jansen et al., 2016):”
QASC: A Dataset for Question Answering via Sentence Composition (2019, methodology): “However, these often require careful manual tuning due to the large variety of reasoning techniques needed for these questions (Boratko et al. 2018) and the large number of facts that often must be composed together (Jansen 2018; Jansen et al. 2016).”
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (2018, methodology): “Previous estimates (Jansen et al., 2016) suggest elementary science questions require an average of 4 pieces of knowledge to answer and explain those answers (here our analysis suggests this is closer to 6), but inference methods tend to have difﬁculty aggregating more than 2 pieces of knowledge…”
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset (2018, background): “ice questions from state and federal science questions for elementary and middle school students.3 Multiple surveys of the knowledge base requirements for accomplishing this task (Clark et al., 2013; Jansen et al., 2016) concluded that advanced inference methods were necessary for many of the questi”
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings (2018, background): “Jansen et al. [2016] has analyzed knowledge and inference requirements for science exam questions.”
WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020, methodology): “…base contains a total of 66 tables whose relations and column structure were developed using a detailed data-driven analysis of the domain requirements (Jansen et al., 2016; Jansen et al., 2018), while several properties tables were drawn from the Aristo Tablestore (Khashabi et al., 2016).”
Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering (2017, background): “In a series of recent papers, elementary science questions are used as a benchmark of AI systems (Khot et al., 2015; Jansen et al., 2016; Clark et al., 2016; Khashabi et al., 2016).”
On the Diversity and Limits of Human Explanations (2021, background): “Reference Task Questions and guidelines Jansen et al. (2016) science QA For each question, we create gold explanations that describe the inference needed to arrive at the correct answer.”
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks (2017, unknown): “In the future, we would like to extend our work in other domains such as science QA (Jansen et al., 2016) and use our work to assist student learning on platforms such as MOOCs.”
Explainable Natural Language Reasoning via Conceptual Unification (2020, background): “A parallel line of research tries to tackle the problem through the construction of explanation-centred corpora, which can facilitate the identiﬁcation of common explanatory patterns (Valentino et al., 2020; Jansen et al., 2018; Jansen, 2017; Jansen et al., 2016).”
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps (2020, background): “, 2018) 112,779 Science Exam QA (Jansen et al., 2016) 363”
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing (2021, background): “[56] Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark.”