Papers by Ian Porada
A Controlled Reevaluation of Coreference Resolution Models (2024.lrec-main)
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| Challenge: | a pretrained language model is used in state-of-the-art coreference resolution models. |
| Approach: | They evaluate five coreference resolution models and control for language model used . they find that encoder-based CR models outperform decoder--based models in accuracy . |
| Outcome: | The encoder-based model outperforms the decoder--based models in accuracy and speed . older model generalizes the best to out-of-domain textual genres . |
Modeling Event Plausibility with Consistent Conceptual Abstraction (2021.naacl-main)
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| Challenge: | Understanding natural language requires common sense, one aspect of which is the ability to discern the plausibility of events. |
| Approach: | They propose a method of forcing model consistency that improves correlation with human plausibility judgements. |
| Outcome: | The proposed method improves correlation with human plausibility judgements. |
Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective (2024.findings-acl)
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| Challenge: | a recent study shows that evaluations of CR models on multiple datasets conflate different factors concerning what is being measured. |
| Approach: | They propose to view evaluations through the lens of measurement modeling . they show that evaluations risk conflating different factors concerning what is being measured . |
| Outcome: | The evaluations on seven datasets show that models that reflect coreference generalization are often correlated with differences in how coreference is defined and operationalized. |
Separately Parameterizing Singleton Detection Improves End-to-end Neural Coreference Resolution (2024.naacl-short)
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| Challenge: | Current end-to-end coreference resolution models combine detection of singleton mentions and antecedent linking into a single step. |
| Approach: | They add a singleton detector to a coarse-to-fine coreference model and design an anaphoricity-aware span embedding and singletont detection loss. |
| Outcome: | The proposed method significantly improves model performance on OntoNotes and four additional datasets. |
ADEPT: An Adjective-Dependent Plausibility Task (2021.acl-long)
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| Challenge: | ADEPT is a large-scale semantic plausibility task that requires a significant degree of world knowledge and common-sense reasoning. |
| Approach: | They propose a large-scale semantic plausibility task that pairs 16 thousand sentences with slightly modified versions obtained by adding an adjective to a noun. |
| Outcome: | The proposed task is easier for humans (85% accuracy), but more difficult for transformer-based models (71% accuracy). |
Can a Gorilla Ride a Camel? Learning Semantic Plausibility from Text (D19-60)
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| Challenge: | Existing work on modeling semantic plausibility has focused on physical plausability but distributional methods fail when tested in supervised settings. |
| Approach: | They propose to use large pretrained language models to model plausibility in supervised settings by extracting attested events from a large corpus and injecting explicit commonsense knowledge into a distributional model. |
| Outcome: | The proposed model is effective in modeling plausibility in a supervised setting. |
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge (2022.naacl-main)
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| Challenge: | Existing evidence suggests that pre-trained Transformers encode commonsense knowledge . however, the extent to which this knowledge is acquired is unclear . |
| Approach: | They inject verbalized knowledge into pre-training minibatches and evaluate generalization . they find generalization does not improve over the course of pre- training from scratch . |
| Outcome: | The proposed model generalizes to supported inferences after pre-training on the injected knowledge. |