Papers by Mahsa Baktashmotlagh
CosMo: Conditional Seq2Seq-based Mixture Model for Zero-Shot Commonsense Question Answering (2020.coling-main)
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| Challenge: | Identifying the implicit causes and effects of a social context is the driving capability of commonsense reasoning. |
| Approach: | They propose a conditional seq2seq-based mixture model which generates context-dependent clauses for commonsense reasoning. |
| Outcome: | The proposed model improves on the current state-of-the-art models by +5.2% over existing models. |
Neural-Symbolic Commonsense Reasoner with Relation Predictors (2021.acl-short)
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| Challenge: | Existing models for commonsense reasoning are limited by their limited set of facts, rendering them unfit for reasoning over new unseen situations and events. |
| Approach: | They propose a neural-symbolic reasoner which can combine commonsense facts with large-scale dynamic CKGs to draw conclusions about ordinary situations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the task of link prediction on CKGs. |