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The goal of a Question Paraphrase Retrieval (QPR) system is to retrieveequivalent questions that result in the same answer as the original question.Such a system can be used to understand and answer rare and noisyreformulations of common questions by mapping them to a set of canonical forms.This has large-scale applications for community Question Answering (cQA) andopen-domain spoken language question answering systems. In this paper wedescribe a new QPR system implemented as a Neural Information Retrieval (NIR)system consisting of a neural network sentence encoder and an approximatek-Nearest Neighbour index for efficient vector retrieval. We also describe ourmechanism to generate an annotated dataset for question paraphrase retrievalexperiments automatically from question-answer logs via distant supervision. Weshow that the standard loss function in NIR, triplet loss, does not performwell with noisy labels. We propose smoothed deep metric loss (SDML) and withour experiments on two QPR datasets we show that it significantly outperformstriplet loss in the noisy label setting.

Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning