Neurips 2022 Deep Bidirectional Language Knowledge Graph Pretraining We presented dragon, a self supervised pretraining method to learn a deeply bidirectional language knowledge model from text and knowledge graphs (kgs) at scale. Pretraining a language model (lm) on text has been shown to help various downstream nlp tasks. recent works show that a knowledge graph (kg) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning.
Deep Bidirectional Language Knowledge Graph Pretraining This work proposes a knowledge enabled language representation model (k bert) with knowledge graphs (kgs), in which triples are injected into the sentences as domain knowledge, which significantly outperforms bert and reveals promising results in twelve nlp tasks. Pretraining a language model (lm) on text has been shown to help various downstream nlp tasks. recent works show that a knowledge graph (kg) can complement text data, offering structured. Specifically, our model takes pairs of text segments and relevant kg subgraphs as input and bidirectionally fuses information from both modalities. we pretrain this model by unifying two self supervised reasoning tasks, masked language modeling and kg link prediction. While we may use any deep bidirectional sequence graph encoder for fenc, for controlled comparison with existing works, we adopt the existing top performing sequence graph architecture, greaselm (zhang et al., 2022), which combines transformers (vaswani et al., 2017) and graph neural networks (gnns) to fuse text kg inputs.
Deep Bidirectional Language Knowledge Graph Pretraining Specifically, our model takes pairs of text segments and relevant kg subgraphs as input and bidirectionally fuses information from both modalities. we pretrain this model by unifying two self supervised reasoning tasks, masked language modeling and kg link prediction. While we may use any deep bidirectional sequence graph encoder for fenc, for controlled comparison with existing works, we adopt the existing top performing sequence graph architecture, greaselm (zhang et al., 2022), which combines transformers (vaswani et al., 2017) and graph neural networks (gnns) to fuse text kg inputs. Dragon is a new foundation model (improvement of bert) that is pre trained jointly from text and knowledge graphs for improved language, knowledge and reasoning capabilities. As the structured knowledge in kg can ground the text and the text can provide the kg with rich context for reasoning, we aim to pretrain a language knowledge model jointly from the text kg pairs (dragon). Specifically, our model takes pairs of text segments and relevant kg subgraphs as input and bidirectionally fuses information from both modalities. we pretrain this model by unifying two self supervised reasoning tasks, masked language modeling and kg link prediction. The document introduces d ragon (deep bidirectional language knowledge graph pretraining), a self supervised model that integrates text and knowledge graphs (kg) for improved natural language processing tasks.
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