Papers by Forough Arabshahi
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules (2021.emnlp-main)
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| Challenge: | Currently, conversational agents lack commonsense reasoning, preventing them from engaging in rich conversations with humans. |
| Approach: | They propose a commonsense reasoning system that uncovers unstated presumptions from user commands satisfying a general template of if-(state), then-(action), because-(goal) They propose to use a transformer-based generative commons sense knowledge base as its source of background knowledge to extract multi-hop reasoning chains from the neural KB. |
| Outcome: | The proposed model achieves a 35% higher success rate than existing methods with human users. |
Look-up and Adapt: A One-shot Semantic Parser (D19-1)
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| Challenge: | Current conversational agents such as Siri, Alexa or Google Assistant do not cater to the specific phrasing of a user or the specific action. |
| Approach: | They propose a semantic parser that generalizes to out-of-domain examples by adapting the logical forms of seen utterances to fit an unseen utterant. |
| Outcome: | The proposed parser improves on one-shot parsing by 68.8% compared to baselines . it adapts the logical forms of seen utterances to fit the unseen utterant . |