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Detecting Hallucinated Content in Conditional Neural Sequence Generation
Neural sequence models can generate highly fluent sentences but recent s...
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Understanding Knowledge Distillation in Non-autoregressive Machine Translation
Non-autoregressive machine translation (NAT) systems predict a sequence ...
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FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Most sequence-to-sequence (seq2seq) models are autoregressive; they gene...
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Handling Syntactic Divergence in Low-resource Machine Translation
Despite impressive empirical successes of neural machine translation (NM...
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Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been ba...
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The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human...
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MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Variational Autoencoder (VAE), a simple and effective deep generative mo...
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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Much work in Natural Language Processing (NLP) has been for resource-ric...
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StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Semantic parsing is the task of transducing natural language (NL) uttera...
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Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Labeled sequence transduction is a task of transforming one sequence int...
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A C-LSTM Neural Network for Text Classification
Neural network models have been demonstrated to be capable of achieving ...
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Category Enhanced Word Embedding
Distributed word representations have been demonstrated to be effective ...
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