3 citations · 9 across the 5 of their papers we have counts for
16 papers
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
Yuling Gu, Yao Fu, Valentina Pyatkin +3
Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We…
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning
Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark
Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would allo…
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge
Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot +6
We present the ARC-DA dataset, a direct-answer ("open response", "freeform") version of the ARC (AI2 Reasoning Challenge) multiple-choice dataset. While ARC has been influential in…
ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language
Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark
Transformers have been shown to emulate logical deduction over natural language theories (logical rules expressed in natural language), reliably assigning true/false labels to cand…
A Dataset for Tracking Entities in Open Domain Procedural Text
Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi Mishra +5
We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog…
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
Dheeraj Rajagopal, Niket Tandon, Bhavana Dalvi +2
We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension. Consider a passage describing a rabbit's life-cycle:…