Abhilasha Ravichander

Language Technologies Institute
School of Computer Science
Carnegie Mellon University

aravicha [at] cs [dot] cmu [dot] edu
Curriculum Vitae
Abhilasha Ravichander

I am a PhD student in the Language Technologies Institute at Carnegie Mellon University. I am very fortunate to be advised by Eduard Hovy and Norman Sadeh.

I received my master's degree from Carnegie Mellon University in 2018, and my bachelor's degree from P.E.S Institute of Technology, Bangalore in 2015. During the summer of 2014, I was a visiting student at the Institute of Mathematical Sciences.

My research interests lie broadly in robustly capturing semantic meaning in text. For more about my work, please see my publications list.


(*) - Equal Contribution

1. EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Abhilasha Ravichander*, Aakanksha Naik*, Carolyn Rose, Eduard Hovy.
arxiv, 2019. [PDF]

2. Challenges in Automatic Question Answering for Privacy Policies
Abhilasha Ravichander, Alan Black, Eduard Hovy, Joel Reidenberg, N. Cameron Russell and Norman Sadeh
AAAI Spring Symposium Series, 2019. To Appear

3. MAPS: Scaling Privacy Compliance Analysis to a Million Apps
Peter Story, Sebastian Zimmeck, Daniel Smullen, Abhilasha Ravichander, Ziqi Wang, Joel Reidenberg, N. Cameron Russell and Norman Sadeh
PETS 2019, To Appear

4. Stress Test Evaluation for Natural Language Inference
Aakanksha Naik*, Abhilasha Ravichander*, Norman Sadeh, Carolyn Rose, Graham Neubig.
27th International Conference on Computational Linguistics (COLING-2018), **Area Chair Favorite Paper**
[PDF , code/data]

5. An Empirical Study of Self-Disclosure in Spoken Dialogue Systems
Abhilasha Ravichander, Alan Black.
19th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL-2018), [PDF].

6. How Would You Say It? Eliciting Lexically Diverse Data for Supervised Semantic Parsing
Abhilasha Ravichander*, Thomas Manzini*, Matthias Grabmair, Jonathan Francis, Graham Neubig, Eric Nyberg.
18th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL-2017). [PDF]

7. Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations
Paul Michel*, Abhilasha Ravichander*, Shruti Rijhwani*.
Workshop on Representation Learning For NLP, Association for Computational Linguistics, 2017 (ACL-2017). [PDF]

8. Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models
Abhilasha Ravichander*, Shruti Rijhwani*, Rajat Kulshreshtha*, Chirag Nagpal, Tadas Baltruˇsaitis, Louis-Phillipe Morency.
arXiv. [PDF]

9. A Machine Learning Approach to Group Dynamic Analysis From A Sequence of Images
Abhilasha Ravichander, Supriya Vijay, Varshini Ramaseshan, Subramanyam Natarajan,
Women in Machine Learning Workshop at NIPS, 2015.

Technical Reports

1. Helping Users Understand Privacy Notices with Automated Question Answering Functionality: An Exploratory Study
Kanthashree Mysore Sathyendra, Abhilasha Ravichander, Peter Garth Story, Alan W Black, Norman Sadeh
Carnegie Mellon University Technical Report CMU-LTI-17-005, Dec 2017 [pdf]

2. Building CMU Magnus from User Feedback
Shrimai Prabhumoye*, Fadi Botros*, Khyathi Chandu*, Samridhi Choudhary*, Esha Keni*, Chaitanya Malaviya*, Thomas Manzini*, Rama Pasumarthi*, Shivani Poddar*, Abhilasha Ravichander*, Zhou Yu, Alan Black.
Alexa Prize Proceedings, 2017. [pdf]


I will be attending ACL, SIGDIAL and YRRSDS 2018 in Melbourne. Ping me if you'd like to chat!
Our work, "Stress Test Evaluation for Natural Language Inference" was an Area Chair Favorite Paper at COLING 2018!
I will be at the Generalization in Deep Learning workshop at NAACL 2018. Ping me if you'd like to chat!
I will be starting my PhD at the Language Technologies Institute, Carnegie Mellon University in Fall 2018.
I am at the Machine Learning Summer School in Tubingen. Let me know if you'd like to meet up!
Our team was selected to participate in the Alexa Prize with a 100,000$ stipend and additional support from Amazon! Congratulations to all the selected teams.
Our work on "A Persistent Homology Approach to Document Clustering" won the best poster award in 10-701 (Introduction to Machine Learning (PhD)).