[38;5;12m                                                                      [39m[38;2;255;187;0m[1m[4mAwesome Natural Language Generation [0m[38;5;14m[1m[4m![0m[38;2;255;187;0m[1m[4mAwesome[0m[38;5;14m[1m[4m (https://awesome.re/badge.svg)[0m[38;2;255;187;0m[1m[4m (https://awesome.re)[0m

[38;5;12m![39m[38;5;14m[1mPiscis Magnus from BL Harley 647[0m[38;5;12m (logo.png)[39m

[38;5;12mNatural[39m[38;5;12m [39m[38;5;12mLanguage[39m[38;5;12m [39m[38;5;12mGeneration[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mbroad[39m[38;5;12m [39m[38;5;12mdomain[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mapplications[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mchat-bots,[39m[38;5;12m [39m[38;5;12mstory[39m[38;5;12m [39m[38;5;12mgeneration,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mdata[39m[38;5;12m [39m[38;5;12mdescriptions.[39m[38;5;12m [39m[38;5;12mThere[39m[38;5;12m [39m[38;5;12mis[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mwide[39m[38;5;12m [39m[38;5;12mspectrum[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mdifferent[39m[38;5;12m [39m[38;5;12mtechnologies[39m[38;5;12m [39m[38;5;12maddressing[39m[38;5;12m [39m[38;5;12mparts[39m[38;5;12m [39m[38;5;12mor[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mwhole[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mNLG[39m[38;5;12m [39m[38;5;12mprocess.[39m[38;5;12m [39m[38;5;12mThis[39m[38;5;12m [39m[38;5;12mlist[39m[38;5;12m [39m[38;5;12maims[39m[38;5;12m [39m
[38;5;12mto[39m[38;5;12m [39m[38;5;12mrepresent[39m[38;5;12m [39m[38;5;12mthis[39m[38;5;12m [39m[38;5;12mdeversity[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mNLG[39m[38;5;12m [39m[38;5;12mapplications[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mtechniques[39m[38;5;12m [39m[38;5;12mby[39m[38;5;12m [39m[38;5;12mproviding[39m[38;5;12m [39m[38;5;12mlinks[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mvarious[39m[38;5;12m [39m[38;5;12mprojects,[39m[38;5;12m [39m[38;5;12mtools,[39m[38;5;12m [39m[38;5;12mresearch[39m[38;5;12m [39m[38;5;12mpapers,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mlearning[39m[38;5;12m [39m[38;5;12mmaterials.[39m

[38;2;255;187;0m[4mContents[0m

[38;5;12m- [39m[38;5;14m[1mDatasets[0m[38;5;12m (#datasets)[39m
[38;5;12m- [39m[38;5;14m[1mDialog[0m[38;5;12m (#dialog)[39m
[38;5;12m- [39m[38;5;14m[1mEvaluation[0m[38;5;12m (#evaluation)[39m
[38;5;12m- [39m[38;5;14m[1mGrammar[0m[38;5;12m (#grammar)[39m
[38;5;12m- [39m[38;5;14m[1mLibraries[0m[38;5;12m (#libraries)[39m
[38;5;12m- [39m[38;5;14m[1mNarrative Generation[0m[38;5;12m (#narrative-generation)[39m
[38;5;12m- [39m[38;5;14m[1mNeural Natural Language Generation[0m[38;5;12m (#neural-natural-language-generation)[39m
[38;5;12m- [39m[38;5;14m[1mPapers and Articles[0m[38;5;12m (#papers-and-articles)[39m
[38;5;12m- [39m[38;5;14m[1mProducts[0m[38;5;12m (#products)[39m
[38;5;12m- [39m[38;5;14m[1mRealizers[0m[38;5;12m (#realizers)[39m
[38;5;12m- [39m[38;5;14m[1mTemplating Languages[0m[38;5;12m (#templating-languages)[39m
[38;5;12m- [39m[38;5;14m[1mVideos[0m[38;5;12m (#videos)[39m

[38;2;255;187;0m[4mDatasets[0m

[38;5;12m- [39m[38;5;14m[1mAlex Context NLG Dataset[0m[38;5;12m (https://github.com/UFAL-DSG/alex_context_nlg_dataset) - A dataset for NLG in dialogue systems in the public transport information domain.[39m
[38;5;12m- [39m[38;5;14m[1mBox-score data[0m[38;5;12m (https://github.com/harvardnlp/boxscore-data/) - This dataset consists of (human-written) NBA basketball game summaries aligned with their corresponding box- and line-scores.[39m
[38;5;12m- [39m[38;5;14m[1mE2E[0m[38;5;12m (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.[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mNeural-Wikipedian[0m[38;5;12m [39m[38;5;12m(https://github.com/pvougiou/Neural-Wikipedian)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mThe[39m[38;5;12m [39m[38;5;12mrepository[39m[38;5;12m [39m[38;5;12mcontains[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mcode[39m[38;5;12m [39m[38;5;12malong[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mthe[39m[38;5;12m [39m[38;5;12mrequired[39m[38;5;12m [39m[38;5;12mcorpora[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12mwere[39m[38;5;12m [39m[38;5;12mused[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12morder[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mbuild[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12msystem[39m[38;5;12m [39m[38;5;12mthat[39m[38;5;12m [39m[38;5;12m"learns"[39m[38;5;12m [39m[38;5;12mhow[39m[38;5;12m [39m[38;5;12mto[39m[38;5;12m [39m[38;5;12mgenerate[39m[38;5;12m [39m[38;5;12mEnglish[39m[38;5;12m [39m[38;5;12mbiographies[39m[38;5;12m [39m[38;5;12mfor[39m[38;5;12m [39m
[38;5;12mSemantic[39m[38;5;12m [39m[38;5;12mWeb[39m[38;5;12m [39m[38;5;12mtriples.[39m
[38;5;12m- [39m[38;5;14m[1mWeatherGov[0m[38;5;12m (https://cs.stanford.edu/~pliang/data/weather-data.zip) - Computer-generated weather forecasts from weather.gov (US public forecast), along with corresponding weather data.[39m
[38;5;12m- [39m[38;5;14m[1mWebNLG[0m[38;5;12m (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.[39m
[38;5;12m- [39m[38;5;14m[1mWikiBio - wikipedia biography dataset[0m[38;5;12m (https://rlebret.github.io/wikipedia-biography-dataset/) - This dataset gathers 728,321 biographies from wikipedia. It aims at evaluating text generation algorithms.[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mThe[0m[38;5;14m[1m [0m[38;5;14m[1mSchema-Guided[0m[38;5;14m[1m [0m[38;5;14m[1mDialogue[0m[38;5;14m[1m [0m[38;5;14m[1mDataset[0m[38;5;12m [39m[38;5;12m(https://github.com/google-research-datasets/dstc8-schema-guided-dialogue)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mThe[39m[38;5;12m [39m[38;5;12mSchema-Guided[39m[38;5;12m [39m[38;5;12mDialogue[39m[38;5;12m [39m[38;5;12m(SGD)[39m[38;5;12m [39m[38;5;12mdataset[39m[38;5;12m [39m[38;5;12mconsists[39m[38;5;12m [39m[38;5;12mof[39m[38;5;12m [39m[38;5;12mover[39m[38;5;12m [39m[38;5;12m20k[39m[38;5;12m [39m[38;5;12mannotated[39m[38;5;12m [39m[38;5;12mmulti-domain,[39m[38;5;12m [39m[38;5;12mtask-oriented[39m[38;5;12m [39m[38;5;12mconversations[39m[38;5;12m [39m[38;5;12mbetween[39m[38;5;12m [39m
[38;5;12ma[39m[38;5;12m [39m[38;5;12mhuman[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12ma[39m[38;5;12m [39m[38;5;12mvirtual[39m[38;5;12m [39m[38;5;12massistant.[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1mThe[0m[38;5;14m[1m [0m[38;5;14m[1mWikipedia[0m[38;5;14m[1m [0m[38;5;14m[1mcompany[0m[38;5;14m[1m [0m[38;5;14m[1mcorpus[0m[38;5;12m [39m[38;5;12m(https://gricad-gitlab.univ-grenoble-alpes.fr/getalp/wikipediacompanycorpus)[39m[38;5;12m [39m[38;5;12m-[39m[38;5;12m [39m[38;5;12mCompany[39m[38;5;12m [39m[38;5;12mdescriptions[39m[38;5;12m [39m[38;5;12mcollected[39m[38;5;12m [39m[38;5;12mfrom[39m[38;5;12m [39m[38;5;12mWikipedia.[39m[38;5;12m [39m[38;5;12mThe[39m[38;5;12m [39m[38;5;12mdataset[39m[38;5;12m [39m[38;5;12mcontains[39m[38;5;12m [39m[38;5;12msemantic[39m[38;5;12m [39m[38;5;12mrepresentations,[39m[38;5;12m [39m[38;5;12mshort,[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;12mlong[39m[38;5;12m [39m[38;5;12mdescriptions[39m[38;5;12m [39m[38;5;12mfor[39m
[38;5;12m51K[39m[38;5;12m [39m[38;5;12mcompanies[39m[38;5;12m [39m[38;5;12min[39m[38;5;12m [39m[38;5;12mEnglish.[39m
[38;5;12m- [39m[38;5;14m[1mYelpNLG[0m[38;5;12m (https://nlds.soe.ucsc.edu/yelpnlg) - YelpNLG provides resources for natural language generation of restaurant reviews.[39m

[38;2;255;187;0m[4mDialog[0m

[38;5;12m- [39m[38;5;14m[1mChatito[0m[38;5;12m (https://github.com/rodrigopivi/Chatito) - Generate datasets for AI chatbots, NLP tasks, named entity recognition or text classification models using a simple DSL![39m
[38;5;12m- [39m[38;5;14m[1mNNDIAL[0m[38;5;12m (https://github.com/shawnwun/NNDIAL) - NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models.[39m
[38;5;12m- [39m[38;5;14m[1mPlato[0m[38;5;12m (https://github.com/uber-research/plato-research-dialogue-system) - This is the Plato Research Dialogue System, a flexible platform for developing conversational AI agents. [39m
[38;5;12m- [39m[38;5;14m[1mRNNLG[0m[38;5;12m (https://github.com/shawnwun/RNNLG) - RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains.[39m
[38;5;12m- [39m[38;5;14m[1mTGen[0m[38;5;12m (https://github.com/UFAL-DSG/tgen) - Statistical NLG for spoken dialogue systems.[39m

[38;2;255;187;0m[4mEvaluation[0m

[38;5;12m- [39m[38;5;14m[1mBLEURT: a Transfer Learning-Based Metric for Natural Language Generation[0m[38;5;12m (https://github.com/google-research/bleurt)[39m
[38;5;12m- [39m[38;5;14m[1mcompare-mt[0m[38;5;12m (https://github.com/neulab/compare-mt) - A tool for holistic analysis of language generations systems.[39m
[38;5;12m- [39m[38;5;14m[1mGEM[0m[38;5;12m (https://gem-benchmark.com/) - a benchmark environment for NLG with a focus on its Evaluation, both through human annotations and automated Metrics.[39m
[38;5;12m- [39m[38;5;14m[1mNLG-eval[0m[38;5;12m (https://github.com/Maluuba/nlg-eval) - Evaluation code for various unsupervised automated metrics for Natural Language Generation.[39m
[38;5;12m- [39m[38;5;14m[1mVizSeq[0m[38;5;12m (https://github.com/facebookresearch/vizseq) - A Visual Analysis Toolkit for Text Generation Tasks.[39m

[38;2;255;187;0m[4mGrammar[0m

[38;5;12m- [39m[38;5;14m[1mOpenCCG[0m[38;5;12m (https://github.com/OpenCCG/openccg) - OpenCCG library for parsing and realization with CCG.[39m
[38;5;12m- [39m[38;5;14m[1mGrammaticalFramework[0m[38;5;12m (http://www.grammaticalframework.org/) - A programming language for multilingual grammar applications.[39m
[38;5;12m- [39m[38;5;14m[1mEasyCCG[0m[38;5;12m (https://github.com/mikelewis0/easyccg) - CCG: All combinators, common grammar format, parsing to logical form, parameter estimation for probabilistic CCG.[39m
[38;5;12m- [39m[38;5;14m[1mCCG Lab[0m[38;5;12m (https://github.com/bozsahin/ccglab) - All combinators, common grammar format, parsing to logical form, parameter estimation for probabilistic CCG.[39m
[38;5;12m- [39m[38;5;14m[1mCCGweb[0m[38;5;12m (https://github.com/texttheater/ccgweb) - A Web platform for parsing and annotation.[39m

[38;2;255;187;0m[4mLibraries[0m

[38;5;12m- [39m[38;5;14m[1mCron Expression Descriptor[0m[38;5;12m (https://github.com/bradymholt/cron-expression-descriptor) - A .NET library that converts cron expressions into human readable descriptions.[39m
[38;5;12m- [39m[38;5;14m[1mNumber Words[0m[38;5;12m (https://github.com/tokenmill/numberwords) - Convert a number to an approximated text expression: from '0.23' to 'less than a quarter'.[39m
[38;5;12m- [39m[38;5;14m[1mWritebot[0m[38;5;12m (https://docs.writebot.app) - A NodeJS library that makes it easier to use GPT-3 by using presets.[39m

[38;2;255;187;0m[4mNarrative Generation[0m

[38;5;12m- [39m[38;5;14m[1mRandom Story Generator[0m[38;5;12m (https://github.com/aherriot/story-generator) - Using Natural Language Generation (NLG) to create a random short story.[39m
[38;5;12m- [39m[38;5;14m[1mTracery[0m[38;5;12m (https://github.com/galaxykate/tracery) - A story-grammar generation library for JavaScript.[39m

[38;2;255;187;0m[4mNeural Natural Language Generation[0m

[38;5;12m- [39m[38;5;14m[1maitextgen[0m[38;5;12m (https://github.com/minimaxir/aitextgen) - A robust Python tool for text-based AI training and generation using GPT-2.[39m
[38;5;12m- [39m[38;5;14m[1mgraph-2-text[0m[38;5;12m (https://github.com/diegma/graph-2-text) - Graph to sequence implemented in Pytorch combining Graph convolutional networks and opennmt-py.[39m
[38;5;12m- [39m[38;5;14m[1mImage Caption Generator[0m[38;5;12m (https://github.com/neural-nuts/image-caption-generator) - A Neural Network based generative model for captioning images using Tensorflow.[39m
[38;5;12m- [39m[38;5;14m[1mlightnlg[0m[38;5;12m (https://github.com/kasnerz/lightnlg) - A minimalistic codebase for finetuning and interacting with NLG models using PyTorch Lightning.[39m
[38;5;12m- [39m[38;5;14m[1mPaperRobot: Incremental Draft Generation of Scientific Ideas[0m[38;5;12m (https://github.com/EagleW/PaperRobot) - We present a PaperRobot who performs as an automatic research assistant.[39m
[38;5;12m- [39m[38;5;14m[1mPPLM[0m[38;5;12m (https://github.com/uber-research/PPLM) - Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.[39m
[38;5;12m- [39m[38;5;14m[1mQuestion Generation using hugstransformers[0m[38;5;12m (https://github.com/patil-suraj/question_generation) - Question generation is the task of automatically generating questions from a text paragraph.[39m
[38;5;12m- [39m[38;5;14m[1mTexar[0m[38;5;12m (https://github.com/asyml/texar) - Texar is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks.[39m
[38;5;12m- [39m[38;5;14m[1mtextgenrnn[0m[38;5;12m (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.[39m
[38;5;12m- [39m[38;5;14m[1mThis Word Does Not Exist[0m[38;5;12m (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.[39m
[38;5;12m- [39m[38;5;14m[1mTransformers[0m[38;5;12m (https://github.com/huggingface/transformers) - State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch.[39m
[38;5;12m- [39m[38;5;14m[1mSummary Generation From Structured Data[0m[38;5;12m (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.[39m

[38;2;255;187;0m[4mPapers and Articles[0m
[38;5;12m- [39m[38;5;14m[1m2022: Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text[0m[38;5;12m (https://arxiv.org/abs/2202.06935)[39m
[38;5;12m- [39m[38;5;14m[1m2021: Vision: NLG Can Help Humanise Data and AI[0m[38;5;12m (https://ehudreiter.com/2021/03/17/vision-nlg-can-help-humanise-data-and-ai/)[39m
[38;5;12m- [39m[38;5;14m[1m2020: The Curious Case of Neural Text Degeneration[0m[38;5;12m (https://openreview.net/forum?id=rygGQyrFvH)[39m
[38;5;12m- [39m[38;5;14m[1m2020: A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems[0m[38;5;12m (https://arxiv.org/abs/2011.03992)[39m
[38;5;12m- [39m[38;5;14m[1m2020: Evaluating the state-of-the-art of End-to-End Natural Language Generation: The E2E NLG challenge[0m[38;5;12m (https://www.sciencedirect.com/science/article/pii/S0885230819300919)[39m
[38;5;12m- [39m[38;5;14m[1m2020: How to generate text: using different decoding methods for language generation with Transformers[0m[38;5;12m (https://huggingface.co/blog/how-to-generate)[39m
[38;5;12m-[39m[38;5;12m [39m[38;5;14m[1m2020:[0m[38;5;14m[1m [0m[38;5;14m[1mNatural[0m[38;5;14m[1m [0m[38;5;14m[1mlanguage[0m[38;5;14m[1m [0m[38;5;14m[1mgeneration:[0m[38;5;14m[1m [0m[38;5;14m[1mThe[0m[38;5;14m[1m [0m[38;5;14m[1mcommercial[0m[38;5;14m[1m [0m[38;5;14m[1mstate[0m[38;5;14m[1m [0m[38;5;14m[1mofthe[0m[38;5;14m[1m [0m[38;5;14m[1mart[0m[38;5;14m[1m [0m[38;5;14m[1min[0m[38;5;14m[1m [0m[38;5;14m[1m2020[0m[38;5;12m [39m
[38;5;12m(https://www.cambridge.org/core/services/aop-cambridge-core/content/view/BA2417D73AF29F8073FF5B611CDEB97F/S135132492000025Xa.pdf/natural_language_generation_the_commercial_state_of_the_art_in_2020.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2020: Turing-NLG: A 17-billion-parameter language model by Microsoft[0m[38;5;12m (https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/)[39m
[38;5;12m- [39m[38;5;14m[1m2019: A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG[0m[38;5;12m (https://www.inlg2019.com/assets/papers/178_Paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2019: A Personalized Data-to-Text Support Tool for Cancer Patients[0m[38;5;12m (https://www.inlg2019.com/assets/papers/28_Paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2019: Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels[0m[38;5;12m (https://www.inlg2019.com/assets/papers/79_Paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2019: Generated Texts Must Be Accurate![0m[38;5;12m (https://ehudreiter.com/2019/09/26/generated-texts-must-be-accurate/)[39m
[38;5;12m- [39m[38;5;14m[1m2019: Hotel Scribe: Generating High Variation Hotel Descriptions[0m[38;5;12m (https://www.inlg2019.com/assets/papers/44_Paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2019: Revisiting Challenges in Data-to-Text Generation with Fact Grounding[0m[38;5;12m (https://www.inlg2019.com/assets/papers/32_Paper.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2017: Survey of the State of the Art in NaturalLanguage Generation: Core tasks, applicationsand evaluation[0m[38;5;12m (https://arxiv.org/pdf/1703.09902.pdf)[39m
[38;5;12m- [39m[38;5;14m[1m2016: Natural Language Generation enhances human decision-making with uncertain information[0m[38;5;12m (https://arxiv.org/pdf/1606.03254.pdf)[39m


[38;2;255;187;0m[4mProducts [0m

[38;5;12m- [39m[38;5;14m[1mAccelerated Text[0m[38;5;12m (https://github.com/tokenmill/accelerated-text) - Automatically generate multiple natural language descriptions of your data varying in wording and structure.[39m
[38;5;12m- [39m[38;5;14m[1mRosaeNLG[0m[38;5;12m (https://rosaenlg.org) - An open-source library for node.js or client side (browser) execution, based on the Pug template engine, to generate texts in English, French, German and Italian.[39m
[38;5;12m- [39m[38;5;14m[1mTwine[0m[38;5;12m (http://twinery.org/) - An open-source tool for telling interactive, nonlinear stories.[39m

[38;2;255;187;0m[4mRealizers[0m

[38;5;12m- [39m[38;5;14m[1mGenl[0m[38;5;12m (https://github.com/kowey/GenI) - Surface realiser (part of a Natural Language Generation system) using Tree Adjoining Grammar.[39m
[38;5;12m- [39m[38;5;14m[1mJSrealB[0m[38;5;12m (https://github.com/rali-udem/JSrealB) - A JavaScript bilingual text realizer for web development.[39m
[38;5;12m- [39m[38;5;14m[1mSimpleNLG[0m[38;5;12m (https://github.com/simplenlg/simplenlg) - Java API for Natural Language Generation.[39m
[38;5;12m- [39m[38;5;14m[1mSimpleNLG DE[0m[38;5;12m (https://github.com/sebischair/SimpleNLG-DE) - German version of SimpleNLG 4.[39m
[38;5;12m- [39m[38;5;14m[1mSimpleNLG-EnFr[0m[38;5;12m (https://github.com/rali-udem/SimpleNLG-EnFr) - SimpleNLG-EnFr 1.1 is a bilingual English/French adaption of SimpleNLG v4.2.[39m

[38;2;255;187;0m[4mTemplating Languages[0m

[38;5;12m- [39m[38;5;14m[1mcalyx[0m[38;5;12m (https://github.com/maetl/calyx) - A Ruby library for generating text with recursive template grammars.[39m
[38;5;12m- [39m[38;5;14m[1mnalgene[0m[38;5;12m (https://github.com/spro/nalgene) - Natural language generation language.[39m
[38;5;12m- [39m[38;5;14m[1mStringTemplate[0m[38;5;12m (https://www.stringtemplate.org/) - Java template engine (with ports for C##, Objective-C, JavaScript, Scala) for generating source code, web pages, emails, or any other formatted text output. [39m

[38;2;255;187;0m[4mVideos[0m

[38;5;12m- [39m[38;5;14m[1mData-To-Text: Generating Textual Summaries of Complex Data - Ehud Reiter[0m[38;5;12m (https://www.youtube.com/watch?v=kFRw-wk5YOA)[39m
[38;5;12m- [39m[38;5;14m[1mImitation Learning and its Application to Natural Language Generation[0m[38;5;12m (https://slideslive.com/38922816/imitation-learning-and-its-application-to-natural-language-generation)[39m
[38;5;12m- [39m[38;5;14m[1mNatural Language Generation (Introduction)[0m[38;5;12m (https://www.youtube.com/watch?v=4fjM72lbJaw)[39m
[38;5;12m- [39m[38;5;14m[1mStrata Data Conference | The future of natural language generation: 2017-2027[0m[38;5;12m (https://www.youtube.com/watch?v=Ls7elVbN8bI)[39m
[38;5;12m- [39m[38;5;14m[1mThe Quest for Automated Story Generation - Mark Riedl[0m[38;5;12m (https://www.youtube.com/watch?v=wgcDUX_BPpk)[39m

[38;2;255;187;0m[4mLicense[0m

[38;5;14m[1m![0m[38;5;12mCC0[39m[38;5;14m[1m (http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)[0m[38;5;12m (http://creativecommons.org/publicdomain/zero/1.0)[39m

[38;5;12mTo the extent possible under law, [39m[38;5;14m[1mTokenMill[0m[38;5;12m (https://www.tokenmill.ai) has waived all copyright and related or neighboring rights to this work.[39m
