Creative and Artistic Text Generation · IJCAI 2019 Tutorial: Creative and Artistic Text Generation...
Transcript of Creative and Artistic Text Generation · IJCAI 2019 Tutorial: Creative and Artistic Text Generation...
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Creative and Artistic Text Generation
Li Juntao and Yan RuiPeking University
Macao, ChinaURL:https://lijuntaopku.github.io/ijcai2019tutorial/
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What Contents Are Included in This Tutorial?
• Poetry Generation
• Story Generation
• Multi-Modal GenerationVisual Storytelling
Visual Poetry Generation
• Other GenresCouplet
Lyrics
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Target Audience
• Ph.D. students or researchers who are working on artistic text generation.
• Anyone who wants to learn how neural approaches (i.e., deeplearning techniques) can be applied to artistic text generation.
• Anyone who wants to build an artistic text generation system (e.g.,story, poetry, couplet) with state-of-the-art neural techniques.
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Outline
• Introduction
• Background Knowledge
• Existing MethodsPoetry GenerationStory Generation
Multi-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
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Task Description of Poetry Generation
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Marjan Ghazvininejad et al. ACL’17Xingxing Zhang et al. EMNLP’14
Input
Output
• Input: a piece of text
• Output: a poem that meets given constraints
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Example System: Jiuge
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https://jiuge.thunlp.cn/jueju_en.html
Settings
Output
Input
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Task Description of Story Generation
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• Definition 1: [Martin et al. AAAI’18]
Automated story generation is the problem of
automatically selecting a sequence of events,
actions, or words that can be told as a story.
• Definition 2: [Xu et al. EMNLP’18]
Input: A short description of a scene or an
event.
Output: A relevant narrative story following the
input.
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Demo Story Generation System
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http://cwc-story.isi.edu/Goldfarb-Tarrant et al., NAACL-HLT ’2019
Input
Output
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Image-Inspired Poetry Generation
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Linli Xu et al. AAAI’18
Input
Output
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Visual Storytelling
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Xin Wang et al., ACL’18Bairui Wang et al., AAAI’19
Input
Output
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Others
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Couplet
Verse
Iambics
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Others
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Ballads
Song Lyrics
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References
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• Zhang, Xingxing, and Mirella Lapata. "Chinese poetry generation with recurrent neural networks.” EMNLP, 2014.• Oliveira, Hugo Gonçalo. "Tra-la-lyrics 2.0: Automatic generation of song lyrics on a semantic domain." Journal of Artificial
General Intelligence, 2015.• Yan, Rui, et al. "Chinese couplet generation with neural network structures." ACL, 2016.• Wang, Qixin, et al. "Chinese song iambics generation with neural attention-based model." IJCAI, 2016.• Singh, Divya, Margareta Ackerman, and Rafael Pérez y Pérez. "A Ballad of the Mexicas: Automated Lyrical Narrative
Writing." ICCC, 2017.• Ghazvininejad, Marjan, et al. "Hafez: an interactive poetry generation system." ACL, System Demonstrations, 2017.• Hopkins, Jack, and Douwe Kiela. "Automatically generating rhythmic verse with neural networks." ACL, 2017.• Martin, Lara J., et al. "Event representations for automated story generation with deep neural nets.“ AAAI, 2018.• Xu, Linli, et al. "How images inspire poems: Generating classical chinese poetry from images with memory networks." AAAI,
2018.• Wang, Xin, et al. "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling." ACL, 2018.• Xu, Jingjing, et al. "A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story
Generation." EMNLP, 2018.• Wang, Bairui, et al. "Hierarchical Photo-Scene Encoder for Album Storytelling." AAAI, 2019.• Goldfarb-Tarrant12, Seraphina, Haining Feng, and Nanyun Peng. "Plan, Write, and Revise: an Interactive System for Open-
Domain Story Generation." NAACL-HLT, 2019.• Zhipeng, Guo, et al. "Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System." ACL, System
Demonstrations, 2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
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Sequence to Sequence Model
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Sutskever, Ilya et al. NIPS, 2014Bahdanau, Dzmitry et al., ICLR, 2015
• Common Used Sequence Generation Method
• Stable and Easy for Training
• Flexibility Encoder-Decoder
Attention
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Convolutional Sequence to Sequence
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Gehring, Jonas, et al., ICML, 2017
• Fast Training
• Strong Language Model for Capturing Long-Range Dependencies
• Bounded CNN Context Window
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Transformer
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Ashish, et al., NIPS, 2017
• Fast Training
• Strong Language Model forCapturing Long-RangeDependencies
• Correlations Learning
• The SOTA Language Model
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Variational Autoencoder
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Zhao, Tiancheng et al., ACL, 2017
• Generative Model
• Wording Diversity
• Intra-Sentence Consistency
• Address Sparsity
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Generative Adversarial Nets
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Yu, Lantao, et al., AAAI, 2017
• One-to-Many Generation
• Enhancing Generator
• Supervision Signal
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Reinforcement Learning
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• Directly Model Discrete Sequence
• Address Loss-Evaluation Mismatch
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References
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• Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. “Sequence to sequence learning with neural networks.” NIPS,2014.
• Diederik P. Kingma and Max Welling. 2015. “Autoencoding variational bayes.” ICLR, 2015.• Bahdanau, Dzmitry et al. “Neural machine translation by jointly learning to align and translate.”ICLR, 2015.• Bowman, Samuel R., et al. “Generating sentences from a continuous space.” arXiv:1511.06349.• Yu, Lantao, et al. "Seqgan: Sequence generative adversarial nets with policy gradient.“ AAAI, 2017.• Zhao, Tiancheng et al. “Learning Discourse-level Diversity for Neural Dialog Models using Conditional
Variational Autoencoders.” ACL, 2017.• Gehring, Jonas, et al. “Convolutional Sequence to Sequence Learning.” ICML, 2017.• Ashish, et al. “Attention is all you need.” NIPS, 2017.• Radford, Alec, et al. “Improving Language Understanding by Generative Pre-Training.” 2018.• Devlin, Jacob, et al. “BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding.” NAACL-HLT, 2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
23IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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Recurrent Neural Model
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Xingxing Zhang et al. EMNLP’14
• TaskChinese Quatrain
• Generation ProcessKeywordsKeywords expansionIncremental generation
Input Phrases Keyword expansion
Line1 OutputIncremental generation
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Recurrent Neural Model
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Xingxing Zhang et al. EMNLP’14
• Convolutional Sentence Model (CSM)
• Recurrent Context Model (RCM)
• Recurrent Generation Model (RGM)
• TrainingCross Entropy Errors
CSM DecoderEncoder
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Planning-Based Recurrent Neural Model
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Zhe Wang et al. COLING’16.
• Keyword Extraction: TextRank Algorithm
• Keyword Expansion: RNNLM-Based Method; Knowledge-Based Method
• Poetry generation: Bidirectional RNN (GRU) Encoder; Attention; RNN (GRU) Decoder
Fig 1 Fig 2
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Iterative Polishing
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Rui Yan et al. IJCAI’16.
• Intention RepresentationCNNRNN
• Sequential GenerationHierarchical RNNCharacter by Character
• Iterative PolishingRe-Generation
RNN1
RNN2
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Interactive Poetry Generation
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Marjan Ghazvininejad et al., ACL’17
Step 1
Step 2
Step 3
• Step 1Search related rhyme words
• Step 2Create a finite-state acceptor (FSA)
• Step 3RNN guided by FSA
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GAN for Poetry Generation
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Lantao Yu et al, AAAI’17
Train Discriminator Train Generator• GAN
Min-Max Game
• GeneratorReinforcement learningMC search
• Discriminator
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Static Memory Model
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Jiyuan Zhang et al., ACL’17
Static Memory
Decoder
Encoder
• EncoderBidirectional RNN
• DecoderOne-Layer RNN
• Memory ContentsPoem Cases
• Memory IndexHidden States
• Memory Combing
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Working Memory Model
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Xiaoyuan Yi et al., IJCAI’18
Decoder
Encoder
Memory
• Line-by-Line Generation
• Bidirectional Encoder and GRU Decoder
• MemoryTopic MemoryHistory MemoryLocal MemoryMemory ReadingMemory Writing
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Conditional Variational Autoencoder
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Xiaopeng Yang et al,. IJCAI’18
Generation Pipeline CVAE Model
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CVAE-GAN Model
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Juntao Li et al., EMNLP’18
Overall Framework CVAE Generator Discriminator
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Mutual Reinforcement Learning
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Xiaoyuan Yi et al., EMNLP’18
• Modeling Poetry Generation as RLProblem
• Fine-Grained Reward DesigningFluency Rewarder (LM)Coherence Rewarder (MI)Meaningfulness Rewarder (TF-IDF)Overall Quality Rewarder (Classifier)
• Mutual Reinforcement LearningTwo GeneratorsInstance-Based MethodDistribution Level Mutual learning
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Stylistic Poetry Generation
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Cheng Yang et al., EMNLP’18
• InputInput sentenceStyle id
• Encoder-Decoder
• Mutual InformationDependency of variables
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Pretraining-Based Model
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Yi Liao et al., arXiv:1907.00151
• Pre-trained ModelGPT
• GenresQuatrainIambicsCouplet
• Fine-Tuning ModelTransformerAuto-regressive Language Model
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Rhetorically Controlled Generation
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Zhiqiang Liu et al., ACL’19
• Modern Poetry Generation
• Manual Control CVAE ModelProcess User Input As Rhetorical Label
• Automatic Control CVAEPredict When Use Rhetoric Label
• Topic MemoryStore Topic Information
• Rhetorically Controlled DecoderGenerate Sentence with Forms of Rhetoric
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Human-Machine Collaborative Generation
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Zhipeng Guo et al., ACL’19
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References
39IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Zhang, Xingxing, and Mirella Lapata. "Chinese poetry generation with recurrent neural networks.” EMNLP,
2014.
• Yan, Rui. "i, Poet: Automatic Poetry Composition through Recurrent Neural Networks with Iterative Polishing
Schema." IJCAI, 2016.
• Wang, Qixin, Tianyi Luo, and Dong Wang. "Can machine generate traditional Chinese poetry? A feigenbaum
test." International Conference on Brain Inspired Cognitive Systems. Springer, Cham, 2016.
• Wang, Zhe, et al. "Chinese poetry generation with planning based neural network." COLING, 2016.
• Yu, Lantao, et al. "Seqgan: Sequence generative adversarial nets with policy gradient.“ AAAI, 2017.
• Oliveira, Hugo Gonçalo. "A survey on intelligent poetry generation: Languages, features, techniques,
reutilisation and evaluation." ACL, 2017.
• Ghazvininejad, Marjan, et al. “Generating topical poetry.” EMNLP, 2016.
• Zhang, Jiyuan, et al. "Flexible and Creative Chinese Poetry Generation Using Neural Memory." ACL, 2017.
• Yi, Xiaoyuan, Ruoyu Li, and Maosong Sun. "Generating chinese classical poems with rnn encoder-
decoder." Springer, Cham, 2017.
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References
40IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Ghazvininejad, Marjan, et al. "Hafez: an interactive poetry generation system." ACL, System Demonstrations,
2017.
• Yi, Xiaoyuan, et al. "Automatic poetry generation with mutual reinforcement learning." EMNLP, 2018.
• Yang, Cheng, et al. "Stylistic chinese poetry generation via unsupervised style disentanglement." EMNLP, 2018.
• Li, Juntao, et al. "Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial
Training." EMNLP, 2018.
• Liao, Yi, et al. "GPT-based Generation for Classical Chinese Poetry." arXiv:1907.00151, 2019.
• Yi, Xiaoyuan, et al. "Chinese Poetry Generation with a Working Memory Model.“ IJCAI, 2018.
• Yang, Xiaopeng, et al. "Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with
Hybrid Decoders.“ IJCAI, 2018.
• Zhipeng, Guo, et al. "Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation
System." ACL, System Demonstrations, 2019.
• Liu, Zhiqiang, et al. "Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation." ACL,
2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
41IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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Coherent Story Generation
42IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Jain, Parag, et al., arXiv:1707.05501, 2017
Task Description
Model
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Event Representations
43IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Lara J. Martin et al., AAAI’18
• Story to Event Sequences5-tuple Event Representations
• Event to Event GenerationEvent-Level Seq2seq
• Event to Story GenerationSeq2seq
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Controllable Story Generation
44IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Peng, Nanyun, et al., Workshop, 2018
• InputHuman inputsControllable factors
• OutputA story that coherent to human inputs
• Ending Valence ControlData labelingSupervised classifierConditional LM for generation
• Storyline ControlKeywords extractorConditional LM for generation
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Hierarchical Story Generation
45IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Fan Angela et al., ACL, 2018
• Hierarchical Generation PipelineGenerating Prompts---Story
• Convolutional Seq2seq For Generating PromptsConventional Convolutional Seq2seq Model
• Gated Multi-Scale AttentionGated self-attention to attend Information at different positionMulti-scale attention to attend information at different granularity
• Prompts FusionResidual Learning Upon pre-trained Convolutional seq2seq model
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Skeleton to Story Generation
46IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Xu, Jingjing, et al., EMNLP, 2018
• Skelton-Based Generative ModuleInput-to-SkeletonSkeleton-to-Sentence
• Skeleton Extraction ModulePretraining on Sentence Compression DatasetReinforcement Learning TrainingTwo Entropy Loss RewardIterative Optimization
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Planning-Based Method
47IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Yao, Lili, et al., AAAI, 2019
System Overview
Planning Method
• Plan and Write
• Static Planning
• Dynamic Planning
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CVAE and Memory Network
48IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Li, Juntao, et al., AAAI, 2019
System Overview
Model
• CVAEWording Novelty
• CacheCoherence
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Plan Write and Revise
49IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Goldfarb-Tarrant, Seraphina et al., NAACL-HLT, 2019
• System Combination
• Cross-Model Mode
• Intra-Model Mode
• Story WriterTitle-to-StoryPlan-and-WritePlan-and-Revise
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BERT Augmented Story Ending Prediction
50IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Li, Zhongyang et al., IJCAI, 2019
• Unsupervised Pre-Training
• Supervised Pre-Training
• Supervised Fine-Tuning
Training Framework
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References
51IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Jain, Parag, et al. "Story Generation from Sequence of Independent Short Descriptions." 2017.
• Martin, Lara J., et al. "Event representations for automated story generation with deep neural nets.“ AAAI, 2018.
• Fan, Angela, Mike Lewis, and Yann Dauphin. "Hierarchical Neural Story Generation." ACL, 2018.
• Wang, Su, et al. "Picking Apart Story Salads.“ EMNLP, 2018.
• Peng, Nanyun, et al. "Towards controllable story generation." Workshop, 2018.
• Xu, Jingjing, et al. "A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation." EMNLP, 2018.
• Angela, Fan et al. “Strategies for Structuring Story Generation”, ACL, 2019.
• Goldfarb-Tarrant12, Seraphina, Haining Feng, and Nanyun Peng. "Plan, Write, and Revise: an Interactive System for
Open-Domain Story Generation." NAACL-HLT, 2019.
• Luo, Fuli, et al. "Learning to Control the Fine-grained Sentiment for Story Ending Generation." ACL, 2019.
• Li, Zhongyang, Xiao Ding, and Ting Liu. "Story Ending Prediction by Transferable BERT." IJCAI, 2019.
• Li, Juntao, et al. “Learning to Write Stories with Thematic Consistency andWording Novelty.” AAAI, 2019.
• Yao, Lili, et al. "Plan-and-write: Towards better automatic storytelling." AAAI, 2019.
• Guan, Jian, Yansen Wang, and Minlie Huang. "Story ending generation with incremental encoding and commonsense
knowledge." AAAI, 2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
52IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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Image Inspired Poetry Generation
53IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Xu, Linli, et al., AAAI, 2018
• Image-Based EncoderCNNBidirectional RNN
• Memory-Based DecoderKeyword ExtractorVector Representations
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Visual Poetry Generation of XiaoIce
54IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Cheng, Wen-Feng, et al., arXiv:1808.03090, 2018
Framework
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Multi-Modal Poetry Generation
55IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Liu, Dayiheng, et al., IJCNN, 2018
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Multi-Adversarial Training
56IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Liu, Bei, et al., ACM, MM, 2018
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Inverse Reinforcement Learning
57IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Wang, Xin, et al., ACL, 2018
Overall Framework Policy Model
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Hierarchical Photo-Scene Encoder
58IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Wang, Bairui, et al., AAAI, 2019
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Hierarchically Structured Reinforcement Learning
59IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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References
60IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Huang, Ting-Hao Kenneth, et al. "Visual storytelling." ACL, 2016.
• Chen, Zhiqian, et al. "Multimodal storytelling via generative adversarial imitation learning." AAAI, 2017.
• Liu, Dayiheng, et al. "A multi-modal Chinese poetry generation model.“ IJCNN, 2018.
• Xu, Linli, et al. "How images inspire poems: Generating classical chinese poetry from images with memory
networks." AAAI, 2018.
• Wang, Xin, et al. "No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling." ACL, 2018.
• Cheng, Wen-Feng, et al. "Image inspired poetry generation in xiaoice." arXiv:1808.03090, 2018.
• Liu, Bei, et al. "Beyond narrative description: Generating poetry from images by multi-adversarial training."
ACM, MM, 2018.
• Wang, Bairui, et al. "Hierarchical Photo-Scene Encoder for Album Storytelling." AAAI, 2019.
• Huang, Qiuyuan, et al. "Hierarchically structured reinforcement learning for topically coherent visual story
generation." AAAI, 2019.
• Ting-Yao Hsu et al. “Visual Story Post-Editing.” ACL, 2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
61IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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Overview
62IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Tasks Main Techniques
Rap Lyric Generation [Potash Peter et al.,15] LSTM + Explicit Templates
Rap Lyric Generation [Malmi Eric et al., 16] Information Retrieval Task
Chinese Song Iambics Generation [Wang et al.,16] Attention-Based Seq2seq
Chinese Couplet Generation [Yan Rui et al., 16] Seq2seq + Attention + Polishing
Rhythmic Verse Generation [Hopkins Jack, 17] Multi-LSTM LM + Finite State Transducers
Theme-Aware Lyrics Generation [Wang Jie, 19] Multi-Channel Seq2seq + LDA
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References
63IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Watanabe, Kento, et al. "Modeling structural topic transitions for automatic lyrics generation.“ ACL, 2014.
• Oliveira, Hugo Gonçalo. "Tra-la-lyrics 2.0: Automatic generation of song lyrics on a semantic domain." Journal
of Artificial General Intelligence, 2015.
• Potash, Peter, Alexey Romanov, and Anna Rumshisky. “Ghostwriter: Using an lstm for automatic rap lyric
generation.” ACL, 2015.
• Wang, Qixin, et al. "Chinese song iambics generation with neural attention-based model." IJCAI, 2016.
• Yan, Rui, et al. "Chinese couplet generation with neural network structures." ACL, 2016.
• Malmi, Eric, et al. "Dopelearning: A computational approach to rap lyrics generation.“ ACM SIGKDD, 2016.
• Singh, Divya, Margareta Ackerman, and Rafael Pérez y Pérez. "A Ballad of the Mexicas: Automated Lyrical
Narrative Writing." ICCC, 2017.
• Hopkins, Jack, and Douwe Kiela. "Automatically generating rhythmic verse with neural networks." ACL, 2017.
• Wang, Jie, and Xinyan Zhao. "Theme-aware generation model for chinese lyrics." arXiv:1906.02134, 2019.
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
64IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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Recent Trends
65IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Learning MethodReinforcement LearningInverse Reinforcement LearningMutual LearningImitation Learning
• Fine-Grained ControllingSentimentEndingForms
• Pre-Training MethodGPTBERTXLNet
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Recent Trends
66IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Better Evaluation Metrics
• Commonsense and Knowledge
• Reasoning
• New Forms of Artistic Text
• Explainability
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Conclusion
67IJCAI 2019 Tutorial: Creative and Artistic Text Generation
• Poetry Generation
• Story Generation
• Multi-Modal GenerationImage-Inspired Poetry GenerationVisual Storytelling
• Other Genres
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Roadmap
• Introduction• Background Knowledge
• Existing MethodsPoetry Generation
Story GenerationMulti-Modal Generation
Other Genres
• Recent Trends and Conclusion
• Q&A
68IJCAI 2019 Tutorial: Creative and Artistic Text Generation
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69IJCAI 2019 Tutorial: Creative and Artistic Text Generation
Thank you!