KENLG-Reading

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KENLG-Reading is an academic research tool that provides a curated reading list and survey for knowledge-enhanced text generation. It offers tutorials, papers, codes, datasets, and leaderboards for researchers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is KENLG-Reading?

KENLG-Reading is a comprehensive repository dedicated to knowledge-enhanced text generation, offering a meticulously curated reading list, tutorials, papers, codes, datasets, and leaderboards. It serves as an invaluable resource for researchers and practitioners in the field, providing a survey published in ACM Computing Survey'22. The repository is actively maintained and updated, ensuring access to the latest advancements and high-citation papers. It covers various aspects of text generation, including basic NLG papers, pretrained language models, controllable generation methods, and knowledge-enhanced techniques using knowledge bases, knowledge graphs, and grounded text.

Best used for

Ideal for professors and researchers who need to stay current with advancements in knowledge-enhanced text generation, find relevant academic papers and associated code, and access comprehensive tutorials. Especially valuable for those looking to deepen their understanding and practical application of NLG techniques.

Common actions

explore research papers

find code examples

learn about text generation

access datasets

review academic surveys

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Capabilities

Key features

Curated reading list
Survey paper access
Tutorials collection
Code links
Dataset links
Leaderboard links

Target Audience

professorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of knowledge-enhanced text generation topics does KENLG-Reading cover?

KENLG-Reading covers a wide range of topics including basic NLG papers, pretrained language generation models, controllable generation methods, and various knowledge-enhanced techniques such as knowledge base, knowledge graph, open knowledge graph, grounded text, and topic-enhanced text generation.

How frequently is the KENLG-Reading repository updated?

The repository is continuously updated with new papers and codes. The last noted update on the README was February 5th, 2022, indicating ongoing maintenance to keep the resources current and relevant for researchers.

Does KENLG-Reading provide access to code and datasets for the listed papers?

Yes, the repository explicitly indicates available code and datasets for many of the listed papers. This makes it a practical resource for researchers looking to replicate results or build upon existing work in knowledge-enhanced text generation.

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Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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