123 citations · 377 across the 10 of their papers we have counts for
23 papers
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
Silin Gao, Jena D. Hwang, Saya Kanno +3
Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs. How…
Deep Bidirectional Language-Knowledge Graph Pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren +4
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering stru…
Synthetic Disinformation Attacks on Automated Fact Verification Systems
Yibing Du, Antoine Bosselut, Christopher D. Manning
Automated fact-checking is a needed technology to curtail the spread of online misinformation. One current framework for such solutions proposes to verify claims by retrieving supp…
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga +4
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM)…
"I'm Not Mad": Commonsense Implications of Negation and Contradiction
Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula +1
Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., "I'm mad at you"), humans can reaso…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…