Papers with semantic embeddings
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator (C18-1)
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| Challenge: | a novel method for obtaining sentence-level embeddings is proposed . the problem of obtaining a semantic embeddable sentence is at the core of understanding languages . |
| Approach: | They propose a method for obtaining sentence-level embeddings by using a sequential encoder-decoder framework. |
| Outcome: | The proposed method outperforms the state-of-the-art on a sentiment analysis task. |
MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)
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| Challenge: | Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora. |
| Approach: | They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training. |
| Outcome: | The proposed method outperforms state-of-the-art unsupervised baselines by a margin. |
Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)
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| Challenge: | Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. |
| Approach: | They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities . |
| Outcome: | The proposed method reduces the distinctiveness of existing embeddings and improves performance. |
Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)
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| Challenge: | Existing models learn user and item embeddings and generate reasons based on these embedds. |
| Approach: | They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons. |
| Outcome: | Extensive experiments on three datasets prove the proposed model is superior to existing models. |
SEMMA: A Semantic Aware Knowledge Graph Foundation Model (2025.emnlp-main)
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Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri, Bo Xiong, Ponnurangam Kumaraguru, Antonio Vergari, Steffen Staab
| Challenge: | Existing Knowledge Graph Foundation Models (KGFMs) rely on graph structure, overlooking the rich semantic signals encoded in textual attributes. |
| Approach: | They propose a dual-module KGFM that integrates transferable textual semantics alongside structure to generate relation identifiers. |
| Outcome: | The proposed model outperforms ULTRA and ULtra in fully inductive link prediction in more challenging generalization settings. |