42 citations · 48 across the 5 of their papers we have counts for
11 papers · 1 filter
CM3: A Causal Masked Multimodal Model of the Internet
Armen Aghajanyan, Bernie Huang, Candace Ross +8
We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new…
Realistic Evaluation Principles for Cross-document Coreference Resolution
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
We point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. W…
Cross-document Coreference Resolution over Predicted Mentions
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challe…
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary
Terra Blevins, Mandar Joshi, Luke Zettlemoyer
Current models for Word Sense Disambiguation (WSD) struggle to disambiguate rare senses, despite reaching human performance on global WSD metrics. This stems from a lack of data fo…
Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling
Arie Cattan, Alon Eirew, Gabriel Stanovsky +2
Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation…
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun +2
Decisions of complex language understanding models can be rationalized by limiting their inputs to a relevant subsequence of the original text. A rationale should be as concise as…