REVIEW 2 cited by
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.
Forward citations
Cited by 2 Pith papers
-
Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba
A BiLSTM-plus-attention model learns query and entity embeddings jointly from search logs and recommends entities for arbitrary Chinese search queries without requiring an explicit entity in the query.
-
Improving Multi-Head Attention with Capsule Networks
Capsule routing after multi-head attention gives small consistent BLEU improvements over Transformer in NMT, with EM routing slightly better than dynamic routing.
Discussion (0). Continue with ORCID to comment.