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QASC: A Dataset for Question Answering via Sentence Composition
TLDR
This work presents a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question, and provides annotation for supporting facts as well as their composition.
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WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference
TLDR
A corpus of explanations for standardized science exams, a recent challenge task for question answering, is presented and an explanation-centered tablestore is provided, a collection of semi-structured tables that contain the knowledge to construct these elementary science explanations.
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Explaining Answers with Entailment Trees
TLDR
ENTAILMENTBANK is created, the first dataset to contain multistep entailment trees, providing a new type of dataset (multistep entails) and baselines, offering a new avenue for the community to generate richer, more systematic explanations.
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WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference
TLDR
This work presents the second iteration of the WorldTree project, a corpus of 5,114 standardized science exam questions paired with large detailed multi-fact explanations that combine core scientific knowledge and world knowledge, and uses this explanation corpus to author a set of 344 high-level science domain inference patterns similar to semantic frames supporting multi-hop inference.
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Discourse Complements Lexical Semantics for Non-factoid Answer Reranking
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…
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Higher-order Lexical Semantic Models for Non-factoid Answer Reranking
TLDR
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.
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TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration
TLDR
The Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base of semi-structured tables.
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Creating Causal Embeddings for Question Answering with Minimal Supervision
TLDR
This work argues that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings, and implements causality as a use case.
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What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
TLDR
This work develops an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges, and compares a retrieval and an inference solver on 212 questions.
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Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?
TLDR
This work empirically characterize the difficulty of building or traversing a graph of sentences connected by lexical overlap, by evaluating chance sentence aggregation quality through 9,784 manually-annotated judgements across knowledge graphs built from three free-text corpora.
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