Papers with Neural architectures
Breaking Through the 80% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information (2020.acl-main)
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| Challenge: | Neural architectures are the current state of the art in Word Sense Disambiguation (WSD) however, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKBs). |
| Approach: | They propose a neural supervised architecture that embeds Lexical Knowledge Bases and exploits pretrained synset embeddings to predict synsets that are not in the training set. |
| Outcome: | The proposed architecture breaks through the 80% ceiling on the concatenation of all standard all-words English evaluation benchmarks. |
Self-Attention Architectures for Answer-Agnostic Neural Question Generation (P19-1)
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| Challenge: | Neural architectures based on self-attention have attracted interest from the research community . a recent study examined the performance of Transformers on a task of Neural Question Generation . |
| Approach: | They propose to adapt Transformers to a task of Neural Question Generation without constraining the model to focus on a specific answer passage. |
| Outcome: | The proposed architectures have obtained significant improvements over the state-of-the-art in several tasks. |