1.2k citations · 1.7k across the 23 of their papers we have counts for
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REGMAPR - Text Matching Made Easy
Siddhartha Brahma
Text matching is a fundamental problem in natural language processing. Neural models using bidirectional LSTMs for sentence encoding and inter-sentence attention mechanisms perform…
Efficiently Processing Workflow Provenance Queries on SPARK
Rajmohan C, Pranay Lohia, Himanshu Gupta +3
In this paper, we investigate how we can leverage Spark platform for efficiently processing provenance queries on large volumes of workflow provenance data. We focus on processing…
Improved Language Modeling by Decoding the Past
Siddhartha Brahma
Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling. We propose a new regularization method based on decoding the last token in t…
Unsupervised Learning of Sentence Representations Using Sequence Consistency
Siddhartha Brahma
Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose ConsSent, a simple yet surprisingly powerful unsupervi…
Improved Sentence Modeling using Suffix Bidirectional LSTM
Siddhartha Brahma
Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirection…
On the scaling of polynomial features for representation matching
Siddhartha Brahma
In many neural models, new features as polynomial functions of existing ones are used to augment representations. Using the natural language inference task as an example, we invest…
SufiSent - Universal Sentence Representations Using Suffix Encodings
Siddhartha Brahma
Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a method to learn such representations by encoding the…