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Invariant Representations for Noisy Speech Recognition
Dmitriy Serdyuk, Kartik Audhkhasi, Philémon Brakel +3
Modern automatic speech recognition (ASR) systems need to be robust under acoustic variability arising from environmental, speaker, channel, and recording conditions. Ensuring such…
A Neural Knowledge Language Model
Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa +1
Current language models have a significant limitation in the ability to encode and decode factual knowledge. This is mainly because they acquire such knowledge from statistical co-…
Context-Dependent Word Representation for Neural Machine Translation
Heeyoul Choi, Kyunghyun Cho, Yoshua Bengio
We first observe a potential weakness of continuous vector representations of symbols in neural machine translation. That is, the continuous vector representation, or a word embedd…
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
Iulian Vlad Serban, Tim Klinger, Gerald Tesauro +4
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic…
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe +4
Sequential data often possesses a hierarchical structure with complex dependencies between subsequences, such as found between the utterances in a dialogue. In an effort to model t…
Iterative Alternating Neural Attention for Machine Reading
Alessandro Sordoni, Philip Bachman, Adam Trischler +1
We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document. Unlike previous models, we…