Search Results for author: Shubham Toshniwal

Found 13 papers, 8 papers with code

On Generalization in Coreference Resolution

1 code implementation CRAC (ACL) 2021 Shubham Toshniwal, Patrick Xia, Sam Wiseman, Karen Livescu, Kevin Gimpel

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.

Coreference Resolution Data Augmentation

Learning Chess Blindfolded

no code implementations1 Jan 2021 Shubham Toshniwal, Sam Wiseman, Karen Livescu, Kevin Gimpel

Motivated by this issue, we consider the task of language modeling for the game of chess.

Game of Chess Language Modelling +1

A Cross-Task Analysis of Text Span Representations

1 code implementation WS 2020 Shubham Toshniwal, Haoyue Shi, Bowen Shi, Lingyu Gao, Karen Livescu, Kevin Gimpel

Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution.

Coreference Resolution Question Answering

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

3 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition

no code implementations27 Jul 2018 Shubham Toshniwal, Anjuli Kannan, Chung-Cheng Chiu, Yonghui Wu, Tara N. Sainath, Karen Livescu

In this paper, we compare a suite of past methods and some of our own proposed methods for using unpaired text data to improve encoder-decoder models.

Automatic Speech Recognition

Hierarchical Multitask Learning for CTC-based Speech Recognition

no code implementations17 Jul 2018 Kalpesh Krishna, Shubham Toshniwal, Karen Livescu

Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder.

Speech Recognition

Multilingual Speech Recognition With A Single End-To-End Model

no code implementations6 Nov 2017 Shubham Toshniwal, Tara N. Sainath, Ron J. Weiss, Bo Li, Pedro Moreno, Eugene Weinstein, Kanishka Rao

Training a conventional automatic speech recognition (ASR) system to support multiple languages is challenging because the sub-word unit, lexicon and word inventories are typically language specific.

Automatic Speech Recognition

Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition

no code implementations5 Apr 2017 Shubham Toshniwal, Hao Tang, Liang Lu, Karen Livescu

We hypothesize that using intermediate representations as auxiliary supervision at lower levels of deep networks may be a good way of combining the advantages of end-to-end training and more traditional pipeline approaches.

Speech Recognition

Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models

1 code implementation20 Oct 2016 Shubham Toshniwal, Karen Livescu

We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion.

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