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20192026
most citedCan a Gorilla Ride a Camel? Learning Semantic Plausibility from Text

9 citations · 12 across the 6 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

The Validity of Coreference-based Evaluations of Natural Language Understanding

Ian Porada

In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the ext…

cs.CL2024

Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution

Ian Porada, Jackie Chi Kit Cheung

Challenge sets such as the Winograd Schema Challenge (WSC) are used to benchmark systems' ability to resolve ambiguities in natural language. If one assumes as in existing work tha…

cs.CL2024

A Controlled Reevaluation of Coreference Resolution Models

Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung

All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the…

cs.CL2023★ 3 cited

Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective

Ian Porada, Alexandra Olteanu, Kaheer Suleman +2

It is increasingly common to evaluate the same coreference resolution (CR) model on multiple datasets. Do these multi-dataset evaluations allow us to draw meaningful conclusions ab…

cs.CL2021

Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge

Ian Porada, Alessandro Sordoni, Jackie Chi Kit Cheung

Transformer models pre-trained with a masked-language-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which t…

cs.CL2021

Modeling Event Plausibility with Consistent Conceptual Abstraction

Ian Porada, Kaheer Suleman, Adam Trischler +1

Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. While distributional models -- most recently pre-tra…