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Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
Xincan Feng, Hidetaka Kamigaito, Katsuhiko Hayashi +1
Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in a…
Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
Xincan Feng, Zhi Qu, Yuchang Cheng +2
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks wh…
Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness
Xincan Feng, Akifumi Yoshimoto
Recent advancements in Natural Language Processing (NLP) have seen Large-scale Language Models (LLMs) excel at producing high-quality text for various purposes. Notably, in Text-To…
Model-based Subsampling for Knowledge Graph Completion
Xincan Feng, Hidetaka Kamigaito, Katsuhiko Hayashi +1
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However, current subsampling approache…