| Challenge: | a new framework for controllable story continuation generation is proposed . we use frames to generate story continuations based on sentence attributes . |
| Approach: | They propose a framework for controlled generation of multiple, diverse outputs . they use sentiment, length, predicates, frames, and automatically-induced clusters as controllable dimensions . |
| Outcome: | The proposed model produces outputs that match target attributes, the authors show . it also yields higher metric scores than previous models, they show ." |
Similar Papers
Changing the Mind of Transformers for Topically-Controllable Language Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing interactive writing assistants do not allow authors to guide text generation in desired topical directions. |
| Approach: | They propose a framework that displays multiple candidate upcoming topics and generates a text generation model that adheres to the chosen topics. |
| Outcome: | The proposed model generates fluent sentences related to the selected topics, as judged by automated metrics and crowdsourced workers. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
Copied to clipboard
| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
Copied to clipboard
| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Controllable Open-ended Question Generation with A New Question Type Ontology (2021.acl-long)
Copied to clipboard
| Challenge: | Existing question types are limited to generating multiple-sense questions . we present a question type-aware question generation framework to generate open-ended questions based on multiple-phrase questions - a task that is less explored . |
| Approach: | They propose a question type-aware question generation framework which predicts question focuses and produces the question. |
| Outcome: | The proposed model improves question quality over competitive comparisons on large-scale datasets. |
Stylized Story Generation with Style-Guided Planning (2021.findings-acl)
Copied to clipboard
| Challenge: | Current storytelling systems focus more on generating stories with coherent plots regardless of the narration style. |
| Approach: | They propose a novel task, stylized story generation, that first plans stylized keywords and then generates the whole story with the guidance of the keywords. |
| Outcome: | The proposed model can generate emotion-driven or event-driven stories based on the ROCStories dataset . |
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models (2020.emnlp-main)
Copied to clipboard
Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
| Challenge: | Existing pre-trained large language models have shown unparalleled generative capabilities, but they are not controllable. |
| Approach: | They propose a framework that uses large-scale language models and adds control to text generation by incorporating an external knowledge base. |
| Outcome: | The proposed model generates more fluent, consistent, and coherent stories with less repetition and higher diversity compared to previous work on the ROC story dataset. |
Contextual Diversity Measure (CDM) for Controllable Story Generation in Large Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing studies on controllable text generation focus on controlling attributes such as sentiment, writing style, and writing style. |
| Approach: | They introduce a metric that quantifies semantic diversity for scenario generation under fixed abstract semantic constraints and validate it through controlled experiments. |
| Outcome: | The proposed metric achieves excellent discrimination accuracy (100% and 91.9%, respectively), with discriminative power up to 5.5 greater than the best baseline. |
A Distributional Lens for Multi-Aspect Controllable Text Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for multi-aspect control suffer from attribute degeneration due to mutual interference of these controllers. |
| Approach: | They propose to use attribute fusion to find the intersections of multiple attributes as their combination for generation. |
| Outcome: | The proposed method outperforms baselines on attribute relevance and text quality and achieves the SOTA. |
Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
Copied to clipboard
| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |
Returning to the Start: Generating Narratives with Related Endpoints (2024.naacl-short)
Copied to clipboard
| Challenge: | RENarGen generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Approach: | They propose a novel novel novel that generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Outcome: | The proposed paradigm generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |