rhetenor/awesome-awesomeness
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Updating conversion, creating readmes

Browse Source
This commit is contained in:
Jonas Zeunert
2024-04-19 23:37:46 +02:00
parent 3619ac710a
commit 08e75b0f0a
635 changed files with 30878 additions and 37344 deletions
Download Patch File Download Diff File Expand all files Collapse all files

27
terminal/nlg
View File

@@ -1,9 +1,9 @@
 Awesome Natural Language Generation !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
 Awesome Natural Language Generation !Awesome (https://awesome.re/badge.svg) (https://awesome.re)
!Piscis Magnus from BL Harley 647 (logo.png)
Natural Language Generation is a broad domain with applications in chat-bots, story generation, and data descriptions. There is a wide spectrum of different technologies addressing parts or the whole of the NLG 
process. This list aims to represent this deversity of NLG applications and techniques by providing links to various projects, tools, research papers, and learning materials.
Natural Language Generation is a broad domain with applications in chat-bots, story generation, and data descriptions. There is a wide spectrum of different technologies addressing parts or the whole of the NLG process. This list aims 
to represent this deversity of NLG applications and techniques by providing links to various projects, tools, research papers, and learning materials.
Contents
@@ -24,18 +24,16 @@
- Alex Context NLG Dataset (https://github.com/UFAL-DSG/alex_context_nlg_dataset) - A dataset for NLG in dialogue systems in the public transport information domain.
- Box-score data (https://github.com/harvardnlp/boxscore-data/) - This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores.
- E2E (http://www.macs.hw.ac.uk/InteractionLab/E2E) - This shared task focuses on recent end-to-end (E2E), data-driven NLG methods, which jointly learn sentence planning and surface realisation from non-aligned 
data.
- Neural-Wikipedian (https://github.com/pvougiou/Neural-Wikipedian) - The repository contains the code along with the required corpora that were used in order to build a system that "learns" how to generate 
English biographies for Semantic Web triples.
- E2E (http://www.macs.hw.ac.uk/InteractionLab/E2E) - This shared task focuses on recent end-to-end (E2E), data-driven NLG methods, which jointly learn sentence planning and surface realisation from non-aligned data.
- Neural-Wikipedian (https://github.com/pvougiou/Neural-Wikipedian) - The repository contains the code along with the required corpora that were used in order to build a system that "learns" how to generate English biographies for 
Semantic Web triples.
- WeatherGov (https://cs.stanford.edu/~pliang/data/weather-data.zip) - Computer-generated weather forecasts from weather.gov (US public forecast), along with corresponding weather data.
- WebNLG (https://github.com/ThiagoCF05/webnlg) - The enriched version of the WebNLG - a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression 
Generation.
- WebNLG (https://github.com/ThiagoCF05/webnlg) - The enriched version of the WebNLG - a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.
- WikiBio - wikipedia biography dataset (https://rlebret.github.io/wikipedia-biography-dataset/) - This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation algorithms.
- The Schema-Guided Dialogue Dataset (https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) - The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, 
task-oriented conversations between a human and a virtual assistant.
- The Wikipedia company corpus (https://gricad-gitlab.univ-grenoble-alpes.fr/getalp/wikipediacompanycorpus) - Company descriptions collected from Wikipedia. The dataset contains semantic representations, short, 
and long descriptions for 51K companies in English.
- The Schema-Guided Dialogue Dataset (https://github.com/google-research-datasets/dstc8-schema-guided-dialogue) - The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between 
a human and a virtual assistant.
- The Wikipedia company corpus (https://gricad-gitlab.univ-grenoble-alpes.fr/getalp/wikipediacompanycorpus) - Company descriptions collected from Wikipedia. The dataset contains semantic representations, short, and long descriptions for
51K companies in English.
- YelpNLG (https://nlds.soe.ucsc.edu/yelpnlg) - YelpNLG provides resources for natural language generation of restaurant reviews.
Dialog
@@ -86,8 +84,7 @@
- textgenrnn (https://github.com/minimaxir/textgenrnn) - Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
- This Word Does Not Exist (https://github.com/turtlesoupy/this-word-does-not-exist) - This is a project allows people to train a variant of GPT-2 that makes up words, definitions and examples from scratch.
- Transformers (https://github.com/huggingface/transformers) - State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.
- Summary Generation From Structured Data (https://github.com/akanimax/natural-language-summary-generation-from-structured-data) - For converting information present in the form of structured data into natural 
language text.
- Summary Generation From Structured Data (https://github.com/akanimax/natural-language-summary-generation-from-structured-data) - For converting information present in the form of structured data into natural language text.
Papers and Articles
- 2022: Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (https://arxiv.org/abs/2202.06935)