Niranjan S. Nayak

AQuPR: Attention based Query Passage Retrieval

Search queries issued over the Web increasingly look like questions, especially as the domain becomes more specific. Finding good response to such queries amounts to finding relevant passages from Web documents. Traditional information retrieval based Web search still matches the query to the words in the entire document. With the advent of machine reading comprehension techniques, Web search is moving more towards identifying the best sentence / group of sentences in the document. We present AQuPR an A ttention based Qu ery P assage R etrieval system to find human acceptable answer containing passages to technology queries issued over the Web. We train character level embeddings for the query and passage pairs, train a deep recurrent network with a novel simplified attention mechanism and incorporate additional signals present in Web documents to improve the performance of such a system. We collect a database of human issued queries along with their answer passages and learn an end to end system to enable automated query resolution. We present results for answering human issued search queries which show considerable promise against basic versions of current generation question answering systems.

Towards bootstrapping Acoustic Models for resource poor Indian languages

There are several challenges while building Automatic Speech Recognition (ASR) system for low resource languages such as Indic languages. One problem is the access to large amounts of training data required to build Acoustic Models (AM) from scratch. In the context of Indian English, another challenge encountered is code-mixing as many Indian speakers are multilingual and exhibit code-mixing in their use of language. Recognizing named entities also poses similar challenges as code-mixing as the entities are often of Hindi origin. In this paper we address the problem of training an AM for Hindi with limited data starting with a well trained English model. We do this in two steps — first we expand the phonesets of the English model to include Hindi phones and train it on samples collected from Indian speakers. We show that this step addresses some of the issues with code-mixing and named entity recognition and also acts as a base model for the second step in which we train a Hindi AM.