mlsanityData to 7 Oct 2026

COMPASS: a Creative Support System that Alerts Novelists to the Unnoticed Missing Contents

arXiv 2202.13151 ↗First posted 26 Feb 2022cs.CLcited by 2

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Abstract (arXiv)

When humans write, they may unintentionally omit some information. Complementing the omitted information using a computer is helpful in providing writing support. Recently, in the field of story understanding and generation, story completion (SC) was proposed to generate the missing parts of an incomplete story. Although its applicability is limited because it requires that the user have prior knowledge of the missing part of a story, missing position prediction (MPP) can be used to compensate for this problem. MPP aims to predict the position of the missing part, but the prerequisite knowledge that "one sentence is missing" is still required. In this study, we propose Variable Number MPP (VN-MPP), a new MPP task that removes this restriction; that is, the task to predict multiple missing sentences or to judge whether there are no missing sentences in the first place. We also propose two methods for this new MPP task. Furthermore, based on the novel task and methods, we developed a creative writing support system, COMPASS. The results of a user experiment involving professional creators who write texts in Japanese confirm the efficacy and utility of the developed system.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 41 of its 87 references that are in the map. First posted on arXiv 26 Feb 2022.

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41 of 87 references.

References · 41

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  6. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  8. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  9. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  10. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  11. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  12. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
29 more
  1. 2018Hierarchical Neural Story Generationcited by 1,182
  2. 2015Teaching Machines to Read and Comprehendcited by 1,039
  3. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  4. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  5. 2016A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Storiescited by 413
  6. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  7. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  8. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  9. 2017Why We Need New Evaluation Metrics for NLGcited by 232
  10. 2018Plan-And-Write: Towards Better Automatic Storytellingcited by 232
  11. 2017Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluationcited by 206
  12. 2015A Neural Conversational Modelcited by 195
  13. 2019Leveraging Pre-trained Checkpoints for Sequence Generation Taskscited by 182
  14. 2019Unifying Human and Statistical Evaluation for Natural Language Generationcited by 127
  15. 2019Strategies for Structuring Story Generationcited by 124
  16. 2020Enabling Language Models to Fill in the Blankscited by 121
  17. 2018Learning to Write with Cooperative Discriminatorscited by 119
  18. 2020A Knowledge-Enhanced Pretraining Model for Commonsense Story Generationcited by 116
  19. 2019Counterfactual Story Reasoning and Generationcited by 87
  20. 2020MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Modelscited by 74
  21. 2018The price of debiasing automatic metrics in natural language evaluationcited by 60
  22. 2018Story Ending Generation with Incremental Encoding and Commonsense Knowledgecited by 60
  23. 2020UNION: An Unreferenced Metric for Evaluating Open-ended Story Generationcited by 60
  24. 2019Plan, Write, and Revise: an Interactive System for Open-Domain Story Generationcited by 43
  25. 2020Narrative Interpolation for Generating and Understanding Storiescited by 19
  26. 2020Neural Language Generation: Formulation, Methods, and Evaluationcited by 13
  27. 2016A Theme-Rewriting Approach for Generating Algebra Word Problemscited by 10
  28. 2019From Plots to Endings: A Reinforced Pointer Generator for Story Ending Generationcited by 5
  29. 2020Narrative Incoherence Detectioncited by 4

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