Siva Reddy

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Montréal Institute of Learning Algorithms ∙ Stanford University ∙ McGill University

Postdoc at the Stanford NLP Group since 2017, Software Engineering Research Intern at Google 2014, Research Engineer at Lexical Computing Ltd from 2010-2012

Modelling Latent Translations for Cross-Lingual Transfer

While achieving state-of-the-art results in multiple tasks and languages...
07/23/2021 ∙ by Edoardo Maria Ponti, et al. ∙ 6 ∙ share

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End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering

We present an end-to-end differentiable training method for retrieval-au...
06/09/2021 ∙ by Devendra Singh Sachan, et al. ∙ 0 ∙ share

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Minimax and Neyman-Pearson Meta-Learning for Outlier Languages

Model-agnostic meta-learning (MAML) has been recently put forth as a str...
06/02/2021 ∙ by Edoardo Maria Ponti, et al. ∙ 0 ∙ share

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Understanding by Understanding Not: Modeling Negation in Language Models

Negation is a core construction in natural language. Despite being very ...
05/07/2021 ∙ by Arian Hosseini, et al. ∙ 0 ∙ share

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Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

In this paper, we propose a new domain adaptation method called back-tra...
04/18/2021 ∙ by Devang Kulshreshtha, et al. ∙ 0 ∙ share

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MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

One of the biggest challenges that prohibit the use of many current NLP ...
12/27/2020 ∙ by Zhi Wen, et al. ∙ 0 ∙ share

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Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback

The ubiquitous nature of chatbots and their interaction with users gener...
10/14/2020 ∙ by Makesh Narsimhan Sreedhar, et al. ∙ 0 ∙ share

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Measuring Systematic Generalization in Neural Proof Generation with Transformers

We are interested in understanding how well Transformer language models ...
09/30/2020 ∙ by Nicolas Gontier, et al. ∙ 6 ∙ share

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Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions

Visual referring expression recognition is a challenging task that requi...
05/04/2020 ∙ by Arjun R Akula, et al. ∙ 11 ∙ share

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StereoSet: Measuring stereotypical bias in pretrained language models

A stereotype is an over-generalized belief about a particular group of p...
04/20/2020 ∙ by Moin Nadeem, et al. ∙ 0 ∙ share

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Building a Neural Semantic Parser from a Domain Ontology

Semantic parsing is the task of converting natural language utterances i...
12/25/2018 ∙ by Jianpeng Cheng, et al. ∙ 0 ∙ share

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CoQA: A Conversational Question Answering Challenge

Humans gather information by engaging in conversations involving a serie...
08/21/2018 ∙ by Siva Reddy, et al. ∙ 4 ∙ share

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Learning an Executable Neural Semantic Parser

This paper describes a neural semantic parser that maps natural language...
11/14/2017 ∙ by Jianpeng Cheng, et al. ∙ 0 ∙ share

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Learning to Paraphrase for Question Answering

Question answering (QA) systems are sensitive to the many different ways...
08/20/2017 ∙ by Li Dong, et al. ∙ 0 ∙ share

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Learning Structured Natural Language Representations for Semantic Parsing

We introduce a neural semantic parser that converts natural language utt...
04/27/2017 ∙ by Jianpeng Cheng, et al. ∙ 0 ∙ share

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Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Existing question answering methods infer answers either from a knowledg...
04/27/2017 ∙ by Rajarshi Das, et al. ∙ 0 ∙ share

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Universal Dependencies to Logical Forms with Negation Scope

Many language technology applications would benefit from the ability to ...
02/10/2017 ∙ by Federico Fancellu, et al. ∙ 0 ∙ share

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Universal Semantic Parsing

Universal Dependencies (UD) offer a uniform cross-lingual syntactic repr...
02/10/2017 ∙ by Siva Reddy, et al. ∙ 0 ∙ share

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Predicting Target Language CCG Supertags Improves Neural Machine Translation

Neural machine translation (NMT) models are able to partially learn synt...
02/03/2017 ∙ by Maria Nădejde, et al. ∙ 0 ∙ share

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Evaluating Induced CCG Parsers on Grounded Semantic Parsing

We compare the effectiveness of four different syntactic CCG parsers for...
09/29/2016 ∙ by Yonatan Bisk, et al. ∙ 0 ∙ share

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DNN-based Speech Synthesis for Indian Languages from ASCII text

Text-to-Speech synthesis in Indian languages has a seen lot of progress ...
08/18/2016 ∙ by Srikanth Ronanki, et al. ∙ 0 ∙ share

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Question Answering on Freebase via Relation Extraction and Textual Evidence

Existing knowledge-based question answering systems often rely on small ...
03/03/2016 ∙ by Kun Xu, et al. ∙ 0 ∙ share

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Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing

One of the limitations of semantic parsing approaches to open-domain que...
01/22/2016 ∙ by Shashi Narayan, et al. ∙ 0 ∙ share

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Siva Reddy

Featured Co-authors

Aaron Courville

Song-Chun Zhu

Andrew McCallum

Chris Dyer

Mirella Lapata

Christopher D. Manning

Anders Søgaard

Ivan Vulić

Kun Xu

Rico Sennrich

Manzil Zaheer

Modelling Latent Translations for Cross-Lingual Transfer

End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering

Minimax and Neyman-Pearson Meta-Learning for Outlier Languages

Understanding by Understanding Not: Modeling Negation in Language Models

Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback

Measuring Systematic Generalization in Neural Proof Generation with Transformers

Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions

StereoSet: Measuring stereotypical bias in pretrained language models

Building a Neural Semantic Parser from a Domain Ontology

CoQA: A Conversational Question Answering Challenge

Learning an Executable Neural Semantic Parser

Learning to Paraphrase for Question Answering

Learning Structured Natural Language Representations for Semantic Parsing

Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks

Universal Dependencies to Logical Forms with Negation Scope

Universal Semantic Parsing

Predicting Target Language CCG Supertags Improves Neural Machine Translation

Evaluating Induced CCG Parsers on Grounded Semantic Parsing

DNN-based Speech Synthesis for Indian Languages from ASCII text

Question Answering on Freebase via Relation Extraction and Textual Evidence

Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing