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Chaitanya Sai Alaparthi – Dual Degree CSE

Chaitanya Sai Alaparthi received his MS  Dual Degree in Computer Science and Engineering (CSE). His research work was supervised by Dr. Manish Shrivastava. Here’s a summary of  Chaitanya Sai Alaparthi’s Learning to Efficiently Rank Passages for Web Question Answering:

Automated Question Answering (AQA) is an attractive variation in search engines where the QA system automatically returns a passage, which answers the user’s question instead of providing several links and letting the user explore for an answer. These AQA systems are an integral part of search engines and voice assistants such as Amazon Alexa, Apple Siri, Microsoft Cortona, Google Assistant, etc., Due to the recent growth in AQA systems, the research in this field has seen a tremendous improvement. Ranking the passages is an important step in an AQA system, where the candidate passages are identified for a question and scored as likely to contain an answer. To explore the various practical approaches for this problem, Microsoft has released a large scale dataset called as Microsoft MAchine Reading & COmprehension (MS MARCO) in 2016. Using the subset of the MS MARCO dataset, it has also organized a challenge called Microsoft AI Challenge India (MSAIC) to provide a platform for researchers and AI practitioners across the country to contribute to this area. In this thesis, we propose different approaches based on biLSTM that we used during our participation in Microsoft AI Challenge India. We show that our best model, based on the biLSTM encoder with coattention mechanism (which we call as coattention encoder), has significantly outperformed the shared baselines and benchmark models. In the second part of this thesis, we discuss and address the two limitations of the coattention encoder. We first extend the coattention encoder to n-grams to make it capture the local context. We further improve the coattention encoder by proposing a simple attention pooling using the query to refine the passage’s representation. We perform all our methods on the MS MARCO passage ranking task and demonstrate that these two methods yields a relative increase of 8.04\% in Mean Reciprocal Rank (MRR@10) compared to the naive coattention encoder. It is the best non-transformer model and is competitive to the BERT base while only having fewer parameters. Additionally, when compared to BERT, our model has a very low ranking latencies.