Deepak Ramachandran


2020

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Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Findings of the Association for Computational Linguistics: EMNLP 2020

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

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Do Language Embeddings capture Scales?
Xikun Zhang | Deepak Ramachandran | Ian Tenney | Yanai Elazar | Dan Roth
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.

2019

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How Large Are Lions? Inducing Distributions over Quantitative Attributes
Yanai Elazar | Abhijit Mahabal | Deepak Ramachandran | Tania Bedrax-Weiss | Dan Roth
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as “Is a lion bigger than a wolf?”. Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.

2015

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Belief Tracking with Stacked Relational Trees
Deepak Ramachandran | Adwait Ratnaparkhi
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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A TV Program Discovery Dialog System using recommendations
Deepak Ramachandran | Mark Fanty | Ronald Provine | Peter Yeh | William Jarrold | Adwait Ratnaparkhi | Benjamin Douglas
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2013

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The Dialog State Tracking Challenge
Jason Williams | Antoine Raux | Deepak Ramachandran | Alan Black
Proceedings of the SIGDIAL 2013 Conference

2012

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Landmark-Based Location Belief Tracking in a Spoken Dialog System
Yi Ma | Antoine Raux | Deepak Ramachandran | Rakesh Gupta
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2010

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Probabilistic Ontology Trees for Belief Tracking in Dialog Systems
Neville Mehta | Rakesh Gupta | Antoine Raux | Deepak Ramachandran | Stefan Krawczyk
Proceedings of the SIGDIAL 2010 Conference