Papers with natural language processing architectures

2 papers
Content- and Topology-Aware Representation Learning for Scientific Multi-Literature (2023.emnlp-main)

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Challenge: Existing approaches focus on learning textual information at sentence- or document-level, but ignore inter-document connections.
Approach: They propose a model that extends representation learning to the multi-document level . it integrates latent semantic and rich relatedness information from topological networks .
Outcome: The proposed model learns latent semantic information from content and rich relatedness information from topological networks.
Analyzing the Surprising Variability in Word Embedding Stability Across Languages (2021.emnlp-main)

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Challenge: Word embeddings are powerful representations that form the foundation of many natural language processing architectures.
Approach: They explore word embedding stability in a wide range of languages to gain insight into their stability.
Outcome: The proposed results provide insights into word embedding stability in English and other languages.

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