215 citations · 297 across the 17 of their papers we have counts for
10 papers · 1 filter
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…
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering
Harsh Jhamtani, Peter Clark
Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to l…
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models
Tushar Khot, Daniel Khashabi, Kyle Richardson +2
We propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvabl…
Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations
Sumithra Bhakthavatsalam, Kyle Richardson, Niket Tandon +1
We present a new knowledge-base of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is…
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
Alon Talmor, Oyvind Tafjord, Peter Clark +2
To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but t…