Abhilasha Ravichander

Language Technologies Institute
School of Computer Science
Carnegie Mellon University

aravicha [at] cs [dot] cmu [dot] edu
GHC 6411
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.

My research interests lie broadly in robustly representing semantic meaning in text. I think a lot about how to estimate and improve model generalization in real-world scenarios, by ensuring models are capable of the kinds of reasoning necessary to understand natural language. This is also concerned with designing better NLP methodology- how to build high-quality datasets and evaluations that reflect meaningful progress towards language understanding.

Additionally, I am interested in the application of these NLP/NLU techniques for social good- essentially, trying to use technology to mitigate some problems created by technology. Through the Usable Privacy Policy Project, I'm engaged in a multi-disciplinary effort to empower internet users to take back control of their privacy.

For more about my work, please see my publications list.

Outside my research interests, I care a lot about helping women interested in getting started with, or furthering, research in natural language processing—feel free to email me! I would be particularly happy to be able to help students from marginalized groups, or less-privileged institutions.


(*) - Equal Contribution

1. EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Abhilasha Ravichander*, Aakanksha Naik*, Carolyn Rose, Eduard Hovy.
2019 Conference on Computational Natural Language Learning, (CoNLL 2019). [PDF]

2. Question Answering for Privacy Policies: Combining Computational and Legal Perspectives
Abhilasha Ravichander, Alan W Black, Shomir Wilson, Thomas Norton and Norman Sadeh.
2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019), [PDF]

3. Exploring Numeracy in Word Embeddings
Abhilasha Ravichander*, Aakanksha Naik*, Carolyn Rose, Eduard Hovy
57th Meeting of Association for Computational Linguistics, 2019 (ACL-2019). [PDF]

4. 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 [PDF]

5. Natural Language Processing for Mobile App Privacy Compliance
Peter Story*, Sebastian Zimmeck*, Abhilasha Ravichander, Daniel Smullen, Ziqi Wang, Joel Reidenberg, N. Cameron Russell and Norman Sadeh
AAAI Spring Symposium Series, 2019. [PDF]

6. Challenges in Automated Question Answering for Privacy Policies
Abhilasha Ravichander, Alan W Black, Eduard Hovy, Joel Reidenberg, N. Cameron Russell and Norman Sadeh
AAAI Spring Symposium Series, 2019. [PDF]

7. 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]

8. 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].

9. 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]

10. 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]

11. 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]

3. 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]

What's New

. .
I will be speaking in a panel on "The Role of Active Privacy Management in a World Where the Consent Model Breaks Down" at CPDP 2020 in Brussels, Belgium.
I will be helping organize the OurCS workshop at CMU. If you are an undergraduate woman, please do consider attending! Funds for hotels and meals will be provided.
I will be spending the summer interning at MSR Montreal with Adam Trischler and Kaheer Suleman, working on teaching machines commonsense reasoning.
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)).