Search Results for author: Jeff Da

Found 9 papers, 2 papers with code

Analyzing Commonsense Emergence in Few-shot Knowledge Models

1 code implementation AKBC 2021 Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, Antoine Bosselut

Our results show that commonsense knowledge models can rapidly adapt from limited examples, indicating that KG fine-tuning serves to learn an interface to encoded knowledge learned during pretraining.

Pretrained Language Models

COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

2 code implementations12 Oct 2020 Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi

Next, we show that ATOMIC 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events.

Knowledge Graphs Natural Language Understanding +1

BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge

no code implementations17 Oct 2019 Jeff Da

Finally, we contribute a method of contextualizing BERT after combining with knowledge base embeddings.

Language Modelling

Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations

no code implementations WS 2019 Jeff Da, Jungo Kasai

Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models.

Knowledge Graphs

Discourse Understanding and Factual Consistency in Abstractive Summarization

no code implementations EACL 2021 Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi

We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary.

Abstractive Text Summarization

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