Papers by Lise Getoor
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue (2022.emnlp-main)
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Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
| Challenge: | Prior studies of task transfer in dialogue consider only 2-4 tasks, focus on multitasks. |
| Approach: | They propose a benchmark for FEw-sample TAsk transfer in open-domain dialogue. |
| Outcome: | The proposed benchmark analyzes the transferability between 132 source-target task pairs and provides a baseline for future work. |
CausalDialogue: Modeling Utterance-level Causality in Conversations (2023.findings-acl)
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| Challenge: | Despite widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans . despite their widespread adoption in society, chatbots have yet not shown natural chat capability . |
| Approach: | They propose a causality-enhanced method to enhance the impact of causality at the utterance level in training neural conversation models. |
| Outcome: | The proposed method improves diversity and agility of loss functions and still needs improvement . the proposed method is based on a CausalDialogue dataset . |
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2023.acl-long)
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Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
| Challenge: | Existing DSI approaches infer latent dialog structure without access to domain knowledge. |
| Approach: | They propose a neural-symbolic approach that injects symbolic knowledge into latent space of a generative neural model. |
| Outcome: | The proposed approach boosts performance over the canonical baselines over three dialog structure induction datasets. |