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Koustuv Sinha

Alumni

Publications

A Simple and Effective Model for Multi-Hop Question Generation
Jimmy Lei Ba
Jamie Ryan Kiros
Geoffrey E Hin-602
Peter W. Battaglia
Jessica Blake
Chandler Hamrick
Vic-613 tor Bapst
Alvaro Sanchez
Vinicius Zambaldi
M. Malinowski
Andrea Tacchetti
David Raposo
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
Prafulla Dhariwal
Arvind Neelakantan
Pranav Shyam … (see 72 more)
Girish Sastry
Koustuv Sinha
Shagun Sodhani
Jin Dong
Joelle Pineau
William L. Hamilton
Clutrr
Nitish Srivastava
Geoffrey Hinton
Alex Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov. 2014
Gabriel Stanovsky
Julian Michael
Luke Zettlemoyer
Dan Su
Yan Xu
Wenliang Dai
Ziwei Ji
Tiezheng Yu
Minghao Tu
Kevin Huang
Guangtao Wang
Jing Huang
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan N. Gomez
Łukasz Kaiser
Illia Polosukhin. 2017
Attention
Petar Veliˇckovi´c
Guillem Cucurull
Arantxa Casanova
Adriana Romero Soriano
Pietro Lio’
Yoshua Bengio
Johannes Welbl
Pontus Stenetorp
Yonghui Wu
Mike Schuster
Quoc Zhifeng Chen
Mohammad Le
Wolfgang Norouzi
Macherey
M. Krikun
Yuan Cao
Qin Gao
William W. Cohen
Jianxing Yu
Xiaojun Quan
Qinliang Su
Jian Yin
Yuyu Zhang
Hanjun Dai
Zornitsa Kozareva
Cheng Zhao
Chenyan Xiong
Corby Rosset
Xia
Paul Song
Bennett Saurabh
Tiwary
Yao Zhao
Xiaochuan Ni
Yuanyuan Ding
Qingyu Zhou
Nan Yang
Furu Wei
Chuanqi Tan
Previous research on automated question gen-001 eration has almost exclusively focused on gen-002 erating factoid questions whose answers ca… (see more)n 003 be extracted from a single document. How-004 ever, there is an increasing interest in develop-005 ing systems that are capable of more complex 006 multi-hop question generation (QG), where an-007 swering the question requires reasoning over 008 multiple documents. In this work, we pro-009 pose a simple and effective approach based on 010 the transformer model for multi-hop QG. Our 011 approach consists of specialized input repre-012 sentations, a supporting sentence classification 013 objective, and training data weighting. Prior 014 work on multi-hop QG considers the simpli-015 fied setting of shorter documents and also ad-016 vocates the use of entity-based graph struc-017 tures as essential ingredients in model design. 018 On the contrary, we showcase that our model 019 can scale to the challenging setting of longer 020 documents as input, does not rely on graph 021 structures, and substantially outperforms the 022 state-of-the-art approaches as measured by au-023 tomated metrics and human evaluation. 024
2021-01-01
(published)
www.semanticscholar.org
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier
Koustuv Sinha
Siva Reddy
Chris Pal
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded… (see more) in the form of natural language. We investigate their systematic generalization abilities on a logical reasoning task in natural language, which involves reasoning over relationships between entities grounded in first-order logical proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to generate natural language proofs. We test the generated proofs for logical consistency, along with the accuracy of the final inference. We observe length-generalization issues when evaluated on longer-than-trained sequences. However, we observe TLMs improve their generalization performance after being exposed to longer, exhaustive proofs. In addition, we discover that TLMs are able to generalize better using backward-chaining proofs compared to their forward-chaining counterparts, while they find it easier to generate forward chaining proofs. We observe that models that are not trained to generate proofs are better at generalizing to problems based on longer proofs. This suggests that Transformers have efficient internal reasoning strategies that are harder to interpret. These results highlight the systematic generalization behavior of TLMs in the context of logical reasoning, and we believe this work motivates deeper inspection of their underlying reasoning strategies.
arxiv.org
A Hierarchical Neural Attention-based Text Classifier
Koustuv Sinha
Yue Dong
Jackie Cheung
Derek Ruths
Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to… (see more) extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
2018-01-01
Conference on Empirical Methods in Natural Language Processing (published)
doi.org