TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)
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| Challenge: | Generating natural and informative texts has been a long-standing problem in NLP. |
| Approach: | They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework. |
| Outcome: | The proposed model can learn what and how to generate on two text generation tasks. |
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
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| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
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| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
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Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
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| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
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Knowledge-Augmented Methods for Natural Language Processing (2022.acl-tutorials)
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Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
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JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs (2021.findings-acl)
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| Challenge: | Existing pre-trained models for knowledgegraph-to-text generation ignore graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. |
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