# Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks

Source: [Microsoft AI Challenge India 2018: Learning to Rank Passages for Web Question Answering with Deep Attention Networks](<https://www.sota2.com/research/paper/68fa65de1e209131cea0cf1d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:37:37.790Z

Authors: Chaitanya Sai Alaparthi

Published: 2019-06-14T07:29:29.000Z

## Abstract

This paper describes our system for The Microsoft AI Challenge India 2018: Ranking Passages for Web Question Answering. The system uses the biLSTM network with co-attention mechanism between query and passage representations. Additionally, we use self attention on embeddings to increase the lexical coverage by allowing the system to take union over different embeddings. We also incorporate hand-crafted features to improve the system performance. Our system achieved a Mean Reciprocal Rank (MRR) of 0.67 on eval-1 dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.06056>)

[PDF](<https://arxiv.org/pdf/1906.06056.pdf>)