My interests include Natural Language Processing, Speech Recognition, Machine Learning and Computer Security.

Papers

Generating Question-Answer Hierarchies
Kalpesh Krishna, Mohit Iyyer
ACL 2019
[arxiv] [project page (with code + data)] [demo] [technical note] [poster]

Syntactically Supervised Transformers for Faster Neural Machine Translation
Nader Akoury, Kalpesh Krishna, Mohit Iyyer
ACL 2019
[arxiv] [code] [poster]

Trick or TReAT: Thematic Reinforcement for Artistic Typography
Purva Tendulkar, Kalpesh Krishna, Ramprasaath R. Selvaraju, Devi Parikh
ICCC 2019 (oral presentation)
[arxiv] [code] [slides] [video] [demo]
(Best Presentation Award)

Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
Kalpesh Krishna, Preethi Jyothi, Mohit Iyyer
EMNLP 2018 (oral presentation, short paper)
[arxiv] [code + data] [slides] [video]

Hierarchical Multitask Learning for CTC-based Speech Recognition
Kalpesh Krishna, Shubham Toshniwal, Karen Livescu
[arxiv] [external video]

A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Kalpesh Krishna, Liang Lu, Kevin Gimpel, Karen Livescu
ICASSP 2018 (Awarded SPS Travel Grant)
[arxiv] [poster]

Bachelor’s Thesis

Constraint Driven Learning
(under guidance of Prof. Preethi Jyothi)
IIT Bombay (2017-2018)
[pdf]

Course Materials

Homework on linguistic probe tasks designed for UMass Amherst’s grad NLP class using AllenNLP.

Research Implementations

Inference Networks for Structured Prediction - A TensorFlow implementation for the multi-label classification experiments in Learning Approximate Inference Networks for Structured Prediction. Also contains experiments on the FIGMENT dataset and a extension to Inference Network training algorithm based on the paper Improved Training of Wasserstein GANs.
[report] [code]

Diversity Sampling in Machine Learning - An implementation of Diverse Beam Search for Neural Networks in Language Modelling. Also contains the original (slightly modified code) for the interactive segmentation experiments in Diverse M-Best Solutions in MRFs.
[report] [code]

Macro Actions in Reinforcement Learning - A suite of five algorithms (including ideas from “Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning”) encouraging agents to repeat actions.
[report] [code]

Single Image Haze Removal - An implementation of He et al. 2009, “Single Image Haze Removal using Dark Channel Prior” and an ongoing implementation of Bahat & Irani 2016, “Blind Dehazing using Internal Patch Recurrence”.
[report] [code]

CNNs for Sentence Classification - A TensorFlow 1.1 implementation of Kim 2014, “Convolutional Neural Networks for Sentence Classification”.
[code]

Brittle Fracture Simulation - Python implementation of O’Brien and Hodgins 1999, “Graphical Modeling and Animation of Brittle Fracture”.
[code]

ECG Signal Analysis - Python implementation of parts of Christopher Buck, Aneesh Sampath 2013, “ECG Signal Analysis for Myocardial Infarction Detection.”.
[code]

Indian Language Datasets

As a part of my RnD project at IIT Bombay, I am releasing the dataset used to train my neural network language models. These have been mined from Wikipedia and I hope this will help further research in language modelling for Indian morphologically rich languages. The folder also contains the original PTB dataset.

  • Malayalam (denoted by ml)
  • Tamil (denoted by ta)
  • Kannada (denoted by kn)
  • Telugu (denoted by te)
  • Hindi (denoted by hi)
  • PTB (denoted by ptb)

All these datasets are compatible with SRILM. Files marked with unk have replaced all singletons with <unk> tokens. Files marked with char are character versions. All datasets have a train, valid and test file. You will find the dataset here.