Papers with artificial intelligence tasks
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. |
Graph Pattern Entity Ranking Model for Knowledge Graph Completion (N19-1)
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| Challenge: | Knowledge graph embedding models are so called-black box and are hard to interpret. |
| Approach: | They propose to use graph patterns to construct an entity ranking system for each graph pattern and evaluate them using a ranking system. |
| Outcome: | The proposed model outperforms other state-of-the-art models on standard metrics such as HITS@n and MRR. |
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
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| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |