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Found 10 papers, 3 papers with code
Explaining Answers with Entailment Trees
Our approach is to generate explanations in the form of entailment trees, namely a tree of multipremise entailment steps from facts that are known, through intermediate conclusions, to the hypothesis of interest (namely the question + answer).
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension.
WIQA: A dataset for ``What if...'' reasoning over procedural text
We introduce WIQA, the first large-scale dataset of {``}What if...{''} questions over procedural text.
Pretrained Language Models for Sequential Sentence Classification
As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document.
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text
To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change.
Exploratory Learning
In multiclass semi-supervised learning (SSL), it is sometimes the case that the number of classes present in the data is not known, and hence no labeled examples are provided for some classes.
WebSets: Extracting Sets of Entities from the Web Using Unsupervised Information Extraction
We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus.
