Papers by Izhak Shafran
MUX-PLMs: Data Multiplexing for High-throughput Language Models (2023.findings-emnlp)
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Vishvak Murahari, Ameet Deshpande, Carlos Jimenez, Izhak Shafran, Mingqiu Wang, Yuan Cao, Karthik Narasimhan
| Challenge: | MUX-PLMs are high-throughput pre-trained language models that can be fine-tuned for any downstream task to yield high-performance. |
| Approach: | They propose to train language models with data multiplexing to achieve 2x/5x inference speedup . they use multiplexers to entangle and disentangle inputs to achieve the same performance . |
| Outcome: | MUX-PLMs achieve 2x/5x inference speedup with 1-4 % drop on broad suite of tasks. |
Audio De-identification - a New Entity Recognition Task (N19-2)
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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
| Challenge: | Named Entity Recognition (NER) is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. |
| Approach: | They propose to use Named Entity Recognition (NER) to detect audio spans with entity mentions in medical records and then use it to evaluate the results. |
| Outcome: | The proposed pipeline is based on a large labeled segment of the Switchboard and Fisher audio datasets and compares it with a benchmark. |
Extracting Symptoms and their Status from Clinical Conversations (P19-1)
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| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
| Outcome: | The proposed models perform well under different conditions and are compared to existing models. |
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. |
Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
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| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |
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 . |