Papers with vanilla RNNs
Neural Syntactic Generative Models with Exact Marginalization (N18-1)
Copied to clipboard
| Challenge: | Recent models have added structure to recurrent neural networks at the cost of giving up exact inference, or using soft structure instead of latent variables. |
| Approach: | They propose a syntactic generative model with exact marginalization that supports dependency parsing and language modeling. |
| Outcome: | The proposed models achieve state-of-the-art for supervised dependency parsing and language modeling. |
An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization (C18-1)
Copied to clipboard
| Challenge: | In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages . we find that NMT model is much better than SMT in terms of character error rate . |
| Approach: | They propose to use NMT models to solve the problem of historical spelling normalization in five languages. |
| Outcome: | The proposed method improves historical spelling normalization for five languages. |
A Hierarchical Latent Structure for Variational Conversation Modeling (N18-1)
Copied to clipboard
| Challenge: | Variational autoencoders suffer from the notorious degeneration problem, according to a new study . utterance drop regularization is an important feature of the hierarchical RNNs . |
| Approach: | They propose a variational hierarchical conversation RNN framework that exploits latent variables and an utterance drop regularization to exploit latent variable. |
| Outcome: | The proposed model outperforms state-of-the-art models on Cornell Movie Dialog and Ubuntu Dialog Corpus. |