# Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons

arXiv: https://arxiv.org/abs/2212.09080 · HTML: https://mlsanity.com/paper/2212.09080/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 18 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Style Transfer in Text: Exploration and Evaluation](https://mlsanity.com/paper/1711.06861/) (2017)
- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://mlsanity.com/paper/2005.10070/) (2020)
- [Table-to-text Generation by Structure-aware Seq2seq Learning](https://mlsanity.com/paper/1711.09724/) (2017)
- [SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation](https://mlsanity.com/paper/2110.10774/) (2021)
- [Legal Document Retrieval using Document Vector Embeddings and Deep Learning](https://mlsanity.com/paper/1805.10685/) (2018)
- [Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity](https://mlsanity.com/paper/1706.01967/) (2017)
- [DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization](https://mlsanity.com/paper/2112.08414/) (2021)
- [Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts](https://mlsanity.com/paper/2011.05675/) (2020)
- [A matter of words: NLP for quality evaluation of Wikipedia medical articles](https://mlsanity.com/paper/1603.01987/) (2016)

## Built on by (most cited first, 5 of 5)

- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain](https://mlsanity.com/paper/2309.17171/) (2023)
- [Fine Tuning Named Entity Extraction Models for the Fantasy Domain](https://mlsanity.com/paper/2402.10662/) (2024)
- [SHADE: Semantic Hypernym Annotator for Domain-specific Entities -- DnD Domain Use Case](https://mlsanity.com/paper/2407.00407/) (2024)
- [Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation](https://mlsanity.com/paper/2607.05260/) (2026)

## Abstract (arXiv)

This paper introduces the Forgotten Realms Wiki (FRW) data set and domain specific natural language generation using FRW along with related analyses. Forgotten Realms is the de-facto default setting of the popular open ended tabletop fantasy role playing game, Dungeons & Dragons. The data set was extracted from the Forgotten Realms Fandom wiki consisting of more than over 45,200 articles. The FRW data set is constituted of 11 sub-data sets in a number of formats: raw plain text, plain text annotated by article title, directed link graphs, wiki info-boxes annotated by the wiki article title, Poincar'e embedding of first link graph, multiple Word2Vec and Doc2Vec models of the corpus. This is the first data set of this size for the Dungeons & Dragons domain. We then present a pairwise similarity comparison benchmark which utilizes similarity measures. In addition, we perform D&D domain specific natural language generation using the corpus and evaluate the named entity classification with respect to the lore of Forgotten Realms.