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Preksha Nema

Preksha Nema

Preksha Nema is a Research Scientist in Advertising-Sciences team in Google Research, Bengaluru, India. Her work revolves around understanding and generating purposeful Ads, with a focus on multilingual and low-resource settings. Broadly, her research interests encompass Natural Language Generation, and Interpretable NLP models. Prior to Google, she was a doctoral student at IIT Madras since 2015. She was awarded Google PhD India Fellowship in 2017. She has worked in Nvidia as a System Software Engineer from 2012-2015. She finished her Bachelors from NIT Nagpur in Computer Science and Engineering in 2012.
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    Preview abstract Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of using Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose the \textbf{T-STAR} model comprising of two components, text-to-AMR and AMR-to-text. We ensure that the intermediate representation is style agnostic, and use style-aware pretraining to improve the AMR-to-text performance. We show that the proposed model outperforms the state of the art TST models with improved content preservation and style accuracy numbers via automatic and human evaluations. View details
    Preview abstract In this paper we present a methodology to analyze users’ concerns and perspectives about privacy at scale. We leverage NLP techniques to process millions of mobile app reviews and extract privacy concerns. Our methodology is composed of a binary classifier that distinguishes between privacy and non-privacy related reviews. We use clustering to gather reviews that discuss similar privacy concerns, and employ summarization metrics to extract representative reviews to summarize each cluster. We apply our methods on 287M reviews for about 2M apps across the 29 categories in Google Play to identify top privacy pain points in mobile apps. We identified approximately 440K privacy related reviews. We find that privacy related reviews occur in all 29 categories, with some issues arising across numerous app categories and other issues only surfacing in a small set of app categories. We show empirical evidence that confirms dominant privacy themes – concerns about apps requesting unnecessary permissions, collection of personal information, frustration with privacy controls, tracking and the selling of personal data. As far as we know, this is the first large scale analysis to confirm these findings based on hundreds of thousands of user inputs. We also observe some unexpected findings such as users warning each other not to install an app due to privacy issues, users uninstalling apps due to privacy reasons, as well as positive reviews that reward developers for privacy friendly apps. Finally we discuss the implications of our method and findings for developers and app stores. View details
    Disentangling Preference Representations for Recommendation Critiquing with β-VAE
    30th ACM International Conference on Information and Knowledge Management (CIKM 2021), ACM, New York
    Preview abstract Modern recommender systems usually embed users and items into a learned vector space representation. Similarity in this space is used to generate recommendations, and recommendation methods are agnostic to the structure of the embedding space. Motivated by the need for recommendation systems to be more transparent and controllable, we postulate that it is beneficial to assign meaning to some of the dimensions of user and item representations. Disentanglement is one technique commonly used for this purpose. We present a novel supervised disentangling approach for recommendation tasks. Our model learns embeddings where attributes of interest are disentangled, while requiring only a very small number of labeled items at training time. The model can then generate interactive and critiquable recommendations for all users, without requiring any labels at recommendation time, and without sacrificing any recommendation performance. Our approach thus provides users with levers to manipulate, critique and fine-tune recommendations, and gives insight into why particular recommendations are made. Given only user-item interactions at recommendation time, we show that it identifies user tastes with respect to the attributes that have been disentangled, allowing for users to manipulate recommendations across these attributes. View details
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