Papers with neural representation learning model

2 papers
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings (P19-1)

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Challenge: Lexical relations are relations between terms in lexicons.
Approach: They propose a neural representation learning model to distinguish lexical relations among term pairs based on hyperspherical relation embeddings.
Outcome: The proposed model outperforms state-of-the-art models on several benchmarks.
Sentence Centrality Revisited for Unsupervised Summarization (P19-1)

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Challenge: Experimental results on three news summarization datasets representative of different languages and writing styles show that our approach outperforms strong baselines by a wide margin.
Approach: They propose an unsupervised approach that uses a popular ranking algorithm to compute node centrality.
Outcome: The proposed approach outperforms baselines on three news summarization datasets representative of different languages and writing styles.

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