Portrait of Siva Reddy

Siva Reddy

Core Academic Member
siva.reddy@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, McGill University, School of Computer Science and Department of Linguistics
Research Topics
Deep Learning
Natural Language Processing
Reasoning
Representation Learning
Website
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Biography

Siva Reddy is an assistant professor at the School of Computer Science and in the Department of Linguistics at McGill University. He completed a postdoc with the Stanford NLP Group in September 2019.

Reddy’s research goal is to enable machines with natural language understanding abilities in order to facilitate applications like question answering and conversational systems. His expertise includes building symbolic (linguistic and induced) and deep learning models for language.

Current Students

Vaibhav Adlakha
PhD - McGill University
Parishad BehnamGhader
Master's Research - McGill University
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Mehar Bhatia
PhD - McGill University
Website
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Spandana Gella
Collaborating researcher
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Gaurav Kamath
PhD - McGill University
Amirhossein Kazemnejad
Master's Research - McGill University
Github
Aditi Khandelwal
PhD - McGill University
Principal supervisor :
Golnoosh Farnadi
Github
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Austin Kraft
PhD - McGill University
Aravind Krishnan
Research Intern - UNIVERSITÄT DES SAARLANDES
Benno Krojer
PhD - McGill University
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Zichao Li
PhD - McGill University
Co-supervisor :
Jackie Cheung
Xing Han Lu
PhD - McGill University
mila@xinghanlu.com
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Andreas Madsen
PhD - Polytechnique Montréal
Principal supervisor :
Sarath Chandar
andreas.madsen@mila.quebec
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Nicholas Meade
PhD - McGill University
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Marius Mosbach
Postdoctorate - McGill University
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Arkil Patel
PhD - McGill University
Principal supervisor :
Dzmitry Bahdanau
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Nooshin Soheililangroodi
Research Intern - McGill University
Karolina Ewa Stańczak
Postdoctorate - McGill University
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Ada Tur Tur
Research Intern - McGill University
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Ivan Vulić
Collaborating researcher - Cambridge University
Alejandra Zambrano
Research Intern - McGill University

Publications

Building a Neural Semantic Parser from a Domain Ontology
Jianpeng Cheng
Siva Reddy
Mirella Lapata
Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be execu… (see more)ted against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.
2018-12-25
ArXiv (preprint)
arxiv.org
arxiv.org
Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini
Nathanael Chambers
Siva Reddy
Xavier R. Holt
Shay B. Cohen
Mark Johnson
Mark Steedman
This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-sour… (see more)ce news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.
2018-12-01
Transactions of the Association for Computational Linguistics (published)
doi.org