Papers by Hagen Soltau
Unsupervised Slot Schema Induction for Task-oriented Dialog (2022.naacl-main)
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| Challenge: | Defining task-specific schemas is the first step of building a task-oriented dialog system. |
| Approach: | They propose an unsupervised approach for slot schema induction from unlabeled dialog corpora using in-domain language models and unsupervised parsing structures. |
| Outcome: | The proposed method shows significant performance improvement on multi-domain and SGD datasets. |
Knowledge-grounded Dialog State Tracking (2022.findings-emnlp)
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| Challenge: | Structured knowledge is encoded implicitly into model parameters for downstream tasks, making training inefficient. |
| Approach: | They propose to perform dialog state tracking grounded on knowledge encoded externally. |
| Outcome: | The proposed method outperforms baseline models in the few-shot learning setting. |
The Medical Scribe: Corpus Development and Model Performance Analyses (2020.lrec-1)
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Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yu-hui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin Stuart Paul
| Challenge: | Existing tools to assist in clinical note generation using audio of provider-patient encounters are lacking. |
| Approach: | They develop an annotation scheme to extract relevant clinical concepts from audio of provider-patient encounters and train a state-of-the-art tagging model. |
| Outcome: | The proposed model is more useful than the F-scores reflect and can be used in clinical notes. |
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)
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Jeffrey Zhao, Yuan Cao, Raghav Gupta, Harrison Lee, Abhinav Rastogi, Mingqiu Wang, Hagen Soltau, Izhak Shafran, Yonghui Wu
| Challenge: | a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions. |
| Approach: | They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training . |
| Outcome: | The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks . |