# Controlled Cue Generation for Play Scripts

arXiv: https://arxiv.org/abs/2112.06953 · HTML: https://mlsanity.com/paper/2112.06953/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Enabling Language Models to Fill in the Blanks](https://mlsanity.com/paper/2005.05339/) (2020)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [Towards Controllable Biases in Language Generation](https://mlsanity.com/paper/2005.00268/) (2020)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation](https://mlsanity.com/paper/2010.01717/) (2020)
- [Exploring Controllable Text Generation Techniques](https://mlsanity.com/paper/2005.01822/) (2020)
- [Simple Fusion: Return of the Language Model](https://mlsanity.com/paper/1809.00125/) (2018)
- [Story Generation from Sequence of Independent Short Descriptions](https://mlsanity.com/paper/1707.05501/) (2017)
- [CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection](https://mlsanity.com/paper/2004.01881/) (2020)
- [Cue Me In: Content-Inducing Approaches to Interactive Story Generation](https://mlsanity.com/paper/2010.09935/) (2020)
- [ScriptWriter: Narrative-Guided Script Generation](https://mlsanity.com/paper/2005.10331/) (2020)
- [Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU](https://mlsanity.com/paper/1910.00912/) (2019)
- [LSTM vs. GRU vs. Bidirectional RNN for script generation](https://mlsanity.com/paper/1908.04332/) (2019)
- [THEaiTRE: Artificial Intelligence to Write a Theatre Play](https://mlsanity.com/paper/2006.14668/) (2020)
- [Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems](https://mlsanity.com/paper/2011.02143/) (2020)

## Built on by (most cited first, 4 of 4)

- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation](https://mlsanity.com/paper/2407.01093/) (2024)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses](https://mlsanity.com/paper/2606.01845/) (2026)

## Abstract (arXiv)

In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues. Using over one million lines of dialogue and cues, we approach the problem of cue generation as a controlled text generation task, and show how cues can be used to enhance the impact of dialogue using a language model conditioned on a dialogue/cue discriminator. In addition, we explore the use of topic keywords and emotions for controlled text generation. Extensive quantitative and qualitative experiments show that language models can be successfully used to generate plausible and attribute-controlled texts in highly specialised domains such as play scripts. Supporting materials can be found at: https://catlab-team.github.io/cuegen.