Papers with recurrent layers
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
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| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings (P18-1)
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| Challenge: | recurrent neural networks have produced significant advances in part-of-speech tagging accuracy . a common feature of these models is the presence of rich initial word encodings . however, word or sub-word information interacts only through subsequent recursive layers . |
| Approach: | They propose to use recurrent neural networks with sentence-level context for initial character and word-based representations. |
| Outcome: | The proposed model has the highest accuracy of all participating systems in the CoNLL 2017 task. |
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (P19-1)
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| Challenge: | Existing approaches to semantic dependency parsing are graph-based and transition-based. |
| Approach: | They propose a second-order semantic dependency parser which takes relationships between two or more edges into account. |
| Outcome: | The proposed algorithm outperforms existing approaches to parsing on graph-based approaches. |
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data (D18-1)
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| Challenge: | InferLite is a lightweight version of InferSent that does not use recurrent layers and can generalize to multiple pre-trained word embeddings. |
| Approach: | They propose a lightweight version of InferSent that does not use recurrent layers and operates on a collection of pre-trained word embeddings. |
| Outcome: | The proposed model outperforms existing models that learn generic embeddings in an unsupervised setting, often requiring several days or weeks to train. |