Papers by Ehsan Qasemi
VIPHY: Probing “Visible” Physical Commonsense Knowledge (2023.findings-emnlp)
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| Challenge: | Existing studies have demonstrated that vision-language models can retain and generalize knowledge, but they do not measure their ability to retain it. |
| Approach: | They build an automatic pipeline to derive a knowledge resource for calibrating and probing vision-language models. |
| Outcome: | The proposed model outperforms the pretrained model on size and spatial tasks. |
PInKS: Preconditioned Commonsense Inference with Minimal Supervision (2022.aacl-main)
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| Challenge: | Existing models for reasoning with preconditions lack data on the problem and lack of support for such reasoning. |
| Approach: | They propose to improve the model for reasoning with preconditions through minimum supervision by PAC-Bayesian informativeness analysis and precision measures. |
| Outcome: | The proposed model improves on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores) it also improves inferences on PAC-Bayesian informativeness analysis, precision measures, and ablation studies. |
PaCo: Preconditions Attributed to Commonsense Knowledge (2022.findings-emnlp)
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| Challenge: | Existing language models can reason with circumstantial preconditions of commonsense knowledge, but they do not understand the circumstancial precondition. |
| Approach: | They propose to use a dataset to examine the ability of existing language models to understand circumstantial preconditions to improve their reasoning with commonsense knowledge. |
| Outcome: | The proposed task shows that human reasoning with preconditions is an open challenge. |
Affective and Dynamic Beam Search for Story Generation (2023.findings-emnlp)
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| Challenge: | AffGen introduces ‘intriguing twists’ in narratives by employing two novel techniques—Dynamic Beam Sizing and Affective Reranking. |
| Approach: | They propose to use dynamic beam sizing and affective reranking to generate interesting stories using two novel techniques. |
| Outcome: | The proposed method outperforms baseline models in generating affectively charged and interesting narratives. |