Vasu Sharma


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BioAMA: Towards an End to End BioMedical Question Answering System
Vasu Sharma | Nitish Kulkarni | Srividya Pranavi | Gabriel Bayomi | Eric Nyberg | Teruko Mitamura
Proceedings of the BioNLP 2018 workshop

In this paper, we present a novel Biomedical Question Answering system, BioAMA: “Biomedical Ask Me Anything” on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed ‘ideal’ answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7% improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.

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Cyclegen: Cyclic consistency based product review generator from attributes
Vasu Sharma | Harsh Sharma | Ankita Bishnu | Labhesh Patel
Proceedings of the 11th International Conference on Natural Language Generation

In this paper we present an automatic review generator system which can generate personalized reviews based on the user identity, product identity and designated rating the user wishes to allot to the review. We combine this with a sentiment analysis system which performs the complimentary task of assigning ratings to reviews based purely on the textual content of the review. We introduce an additional loss term to ensure cyclic consistency of the sentiment rating of the generated review with the conditioning rating used to generate the review. The introduction of this new loss term constraints the generation space while forcing it to generate reviews adhering better to the requested rating. The use of ‘soft’ generation and cyclic consistency allows us to train our model in an end to end fashion. We demonstrate the working of our model on product reviews from Amazon dataset.


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Segmentation Guided Attention Networks for Visual Question Answering
Vasu Sharma | Ankita Bishnu | Labhesh Patel
Proceedings of ACL 2017, Student Research Workshop


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Automatic tagging and retrieval of E-Commerce products based on visual features
Vasu Sharma | Harish Karnick
Proceedings of the NAACL Student Research Workshop


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Analyzing Newspaper Crime Reports for Identification of Safe Transit Paths
Vasu Sharma | Rajat Kulshreshtha | Puneet Singh | Nishant Agrawal | Akshay Kumar
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop