Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Zhang, Saizheng"'
Autor:
Yang, Zhilin, Qi, Peng, Zhang, Saizheng, Bengio, Yoshua, Cohen, William W., Salakhutdinov, Ruslan, Manning, Christopher D.
Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the
Externí odkaz:
http://arxiv.org/abs/1809.09600
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile info
Externí odkaz:
http://arxiv.org/abs/1801.07243
Autor:
Serban, Iulian V., Sankar, Chinnadhurai, Germain, Mathieu, Zhang, Saizheng, Lin, Zhouhan, Subramanian, Sandeep, Kim, Taesup, Pieper, Michael, Chandar, Sarath, Ke, Nan Rosemary, Rajeswar, Sai, de Brebisson, Alexandre, Sotelo, Jose M. R., Suhubdy, Dendi, Michalski, Vincent, Nguyen, Alexandre, Pineau, Joelle, Bengio, Yoshua
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through bot
Externí odkaz:
http://arxiv.org/abs/1801.06700
Autor:
Yang, Zhilin, Zhang, Saizheng, Urbanek, Jack, Feng, Will, Miller, Alexander H., Szlam, Arthur, Kiela, Douwe, Weston, Jason
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning procedure called Mechanical Turker Descent (MT
Externí odkaz:
http://arxiv.org/abs/1711.07950
Autor:
Serban, Iulian V., Sankar, Chinnadhurai, Germain, Mathieu, Zhang, Saizheng, Lin, Zhouhan, Subramanian, Sandeep, Kim, Taesup, Pieper, Michael, Chandar, Sarath, Ke, Nan Rosemary, Rajeshwar, Sai, de Brebisson, Alexandre, Sotelo, Jose M. R., Suhubdy, Dendi, Michalski, Vincent, Nguyen, Alexandre, Pineau, Joelle, Bengio, Yoshua
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through bot
Externí odkaz:
http://arxiv.org/abs/1709.02349
Autor:
Subramanian, Sandeep, Wang, Tong, Yuan, Xingdi, Zhang, Saizheng, Bengio, Yoshua, Trischler, Adam
We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-
Externí odkaz:
http://arxiv.org/abs/1706.04560
Autor:
Yuan, Xingdi, Wang, Tong, Gulcehre, Caglar, Sordoni, Alessandro, Bachman, Philip, Subramanian, Sandeep, Zhang, Saizheng, Trischler, Adam
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maxi
Externí odkaz:
http://arxiv.org/abs/1705.02012
Autor:
Zhang, Ying, Pezeshki, Mohammad, Brakel, Philemon, Zhang, Saizheng, Bengio, Cesar Laurent Yoshua, Courville, Aaron
Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidde
Externí odkaz:
http://arxiv.org/abs/1701.02720
The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, wh
Externí odkaz:
http://arxiv.org/abs/1610.09038