Rosni Vasu

selected publications

1. AAAI

   Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts.

   Basu, Chandrayee,  Vasu, Rosni,  Yasunaga, Michihiro and 1 more author

   In Association for the Advancement of Artificial Intelligence, AAAI 2023 Feb 2023

   Abs PDF Code Poster

   Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated base- line. Furthermore, our position-aware control enhances the model’s ability to generate better simplification than the position-agnostic version. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.

2. HUMAN.AI@AAAI

   Automatic Medical Text Simplification: Challenges of Data Quality and Curation.

   Basu, Chandrayee,  Vasu, Rosni,  Yasunaga, Michihiro and 2 more authors

   In Human Partnership with Medical Artificial Intelligence (HUMAN.AI) ), AAAI Fall 2021 Symposium Nov 2021

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   Health Literacy is the degree to which individuals can comprehend basic health information needed to make appropriate health decisions. The topmost reason for low health literacy is the vocabulary gap between providers and patients. Automatic medical text simplification can contribute to improving health literacy by assisting providers with patientfriendly communication, improving health data search, and making online medical texts more accessible. It is, however, extremely challenging to curate quality corpus for this natural language processing (NLP) task. In this position paper, we observe that, despite recent research efforts, existing open corpora for medical text simplification are poor in quality and size. In order to match the progress in general text simplification and style transfer, we must leverage careful crowdsourcing. We discuss the challenges of naive crowd-sourcing. We propose that careful crowd-sourcing for medical text simplification is possible, when combined with automatic data labeling, a well-designed expert-layman collaboration framework, and context-dependent crowd-sourcing instructions.

3. PAL@AAAI

   Consent Recommender System: A Case Study on LinkedIn Settings

   Rosni, KV, Shukla, Manish,  Banahatti, Vijayanand and 1 more author

   Mar 2019

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   Privacy is an increasing concern in the digital world, especially when it has become a common knowledge that even high profile enterprises process data without data-subject’s consent. In certain cases where data-subject’s consent was taken, it was not linked to the proper purpose of processing. To address this growing concern, newer privacy regulations and laws are emerging to empower a data-subject with informed and explicit consent through which she can allow or revoke usage of her personal data. However, it has been shown that privacy self-management does not provide the expected results. This is mainly due to information overload as data-subjects use multiple services entailing variety of purposes, and hence, resulting in a very large number of consent requests. This may lead to consent fatigue as data-subject is now expected to provide informed consent for each associated purpose. The consent fatigue in data-subjects can lead to either incorrect decision making or opting for default values provided by the enterprise, and thus, defeating the purpose of new data privacy regulations. In this work, we discuss the factors influencing the informed consent of a data-subject. Further, we propose a ‘consent recommender system’ based on Factorization Machines (FMs) to assist the data-subject and thereby avoiding consent fatigue. Our consent recommender system effectively models the interaction between the different factors which influence a data-subject’s informed consent. We discuss how this setup extends for cold start data-subjects facing the decision problem with consent requests from multiple enterprises. Additionally, we demonstrate the scenario of consent recommendation as a prediction problem with minimum attributes available from LinkedIn’s privacy settings.

4. RecSysKTL

   Divide and Transfer: Understanding Latent Factors for Recommendation Tasks.

   Rao, Vidyadhar, Rosni, KV,  and Padmanabhan, Vineet

   In RecSysKTL Workshop co-located with ACM RecSys Aug 2017

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   Traditionally, latent factor models have been the most successful techniques to build recommendation systems. While the key is to capture the user interests effectively, most research is focused on learning latent factors under cold-start and data sparsity situations. Our work brings a complementary approach to the previous studies showing that understanding the semantic aspects of latent factors could give a hint on how to transfer useful knowledge from auxiliary domain(s) to the target domain. In this work, we propose a collaborative filtering technique that can effectively utilize the user preferences and content information. In our approach, we follow a divide and transfer strategy that could derive semantically meaningful latent factors and utilize only the appropriate components for recommendations. We demonstrate the effectiveness of our approach due to improved latent feature space in both single and cross-domain tasks. Further, we also show its robustness by performing extensive experiments under cold-start and data sparsity contexts.