Papers with NELL
IIT-KGP at COIN 2019: Using pre-trained Language Models for modeling Machine Comprehension (D19-60)
Copied to clipboard
| Challenge: | Using pre-trained language models, we can model machine comprehension using commonsense reasoning. |
| Approach: | They propose a machine comprehension model that leverages pre-trained language models over commonsense knowledge bases. |
| Outcome: | The proposed model improves on baseline models and other commonsense knowledge bases. |
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
Copied to clipboard
| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
| Outcome: | The proposed method outperforms state-of-the-art methods in few-shot scenarios on two public datasets from Freebase and NELL. |
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing reasoning methods for sparse KGs are incomplete and lack of evidential paths to target entities makes multi-hop reasoning difficult. |
| Approach: | They propose a multi-hop reasoning model over sparse KGs to solve this problem . they use latent prediction of embedding-based models to make the model perform more potential path search over sparses . |
| Outcome: | The proposed method outperforms state-of-the-art models on five datasets from Freebase, NELL and Wikidata. |