Search Results for author: Anitha Kannan

Found 29 papers, 7 papers with code

Extrinsically-Focused Evaluation of Omissions in Medical Summarization

no code implementations14 Nov 2023 Elliot Schumacher, Daniel Rosenthal, Varun Nair, Luladay Price, Geoffrey Tso, Anitha Kannan

In safety-critical domains such as medicine, more rigorous evaluation is required, especially given the potential for LLMs to omit important information in the resulting summary.

Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue

no code implementations6 Jun 2023 Maksim Eremeev, Ilya Valmianski, Xavier Amatriain, Anitha Kannan

For high-stake domains that are also knowledge-rich, we show how to use knowledge to (a) identify which rare tokens that appear in both source and reference are important and (b) uplift their conditional probability.

Text Generation

Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models

no code implementations10 May 2023 Varun Nair, Elliot Schumacher, Anitha Kannan

A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient.

Decision Making Dialogue Understanding

CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants

no code implementations27 Apr 2023 Albert Yu Sun, Varun Nair, Elliot Schumacher, Anitha Kannan

This scenario-guided approach produces a diverse training set of rule-violating conversations, and it provides chatbot designers greater control over the classification process.

Chatbot

Dialogue-Contextualized Re-ranking for Medical History-Taking

no code implementations4 Apr 2023 Jian Zhu, Ilya Valmianski, Anitha Kannan

We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP).

Language Modelling Re-Ranking

Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach

1 code implementation6 Oct 2022 Mengqian Wang, Ilya Valmianski, Xavier Amatriain, Anitha Kannan

This paper presents an approach that tackles the problem of learning to classify medical dialogue into functional sections without requiring a large number of annotations.

Sentence

MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

1 code implementation17 Nov 2021 Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.

Sentence

Adding more data does not always help: A study in medical conversation summarization with PEGASUS

1 code implementation15 Nov 2021 Varun Nair, Namit Katariya, Xavier Amatriain, Ilya Valmianski, Anitha Kannan

Summarized conversations are used to facilitate patient hand-offs between physicians, and as part of providing care in the future.

Active Learning Transfer Learning

Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization

no code implementations NAACL (NLPMC) 2021 Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan

In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.

Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions

no code implementations12 Nov 2020 Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain, Serena Yeung, Anitha Kannan

We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).

Active Learning Descriptive

Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.

no code implementations Findings of the Association for Computational Linguistics 2020 Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan

Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.

Decision Making Natural Language Understanding

Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures

no code implementations18 Sep 2020 Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan

Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge.

Decision Making Natural Language Understanding

COVID-19 in differential diagnosis of online symptom assessments

no code implementations7 Aug 2020 Anitha Kannan, Richard Chen, Vignesh Venkataraman, Geoffrey J. Tso, Xavier Amatriain

Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today.

Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs

no code implementations4 Aug 2020 Clara H. McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them.

Question Answering Question Similarity +3

The accuracy vs. coverage trade-off in patient-facing diagnosis models

no code implementations11 Dec 2019 Anitha Kannan, Jason Alan Fries, Eric Kramer, Jen Jen Chen, Nigam Shah, Xavier Amatriain

A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.

Domain-Relevant Embeddings for Medical Question Similarity

no code implementations9 Oct 2019 Clara McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain

The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique.

Question Answering Question Similarity +2

Open Set Medical Diagnosis

no code implementations7 Oct 2019 Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, Xavier Amatriain

Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i. e. that models will only encounter conditions on which they have been trained.

Medical Diagnosis Open Set Learning

Classification As Decoder: Trading Flexibility For Control In Neural Dialogue

no code implementations4 Oct 2019 Sam Shleifer, Manish Chablani, Namit Katariya, Anitha Kannan, Xavier Amatriain

Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model.

Classification General Classification

Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations

no code implementations ICML 2018 Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra

Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e. g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is intractable due to exponentially large output spaces (e. g. all English sentences).

Multi-Label Classification Question Generation +3

Learning from the experts: From expert systems to machine-learned diagnosis models

no code implementations21 Apr 2018 Murali Ravuri, Anitha Kannan, Geoffrey J. Tso, Xavier Amatriain

In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned.

Medical Diagnosis

Tackling Over-pruning in Variational Autoencoders

no code implementations9 Jun 2017 Serena Yeung, Anitha Kannan, Yann Dauphin, Li Fei-Fei

The so-called epitomes of this model are groups of mutually exclusive latent factors that compete to explain the data.

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

1 code implementation NeurIPS 2017 Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra

In contrast, discriminative dialog models (D) that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses.

Informativeness Metric Learning +2

LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

1 code implementation5 Mar 2017 Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh

We present LR-GAN: an adversarial image generation model which takes scene structure and context into account.

Image Generation

Transformation-Based Models of Video Sequences

no code implementations29 Jan 2017 Joost van Amersfoort, Anitha Kannan, Marc'Aurelio Ranzato, Arthur Szlam, Du Tran, Soumith Chintala

In this work we propose a simple unsupervised approach for next frame prediction in video.

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