Story Realization: Expanding Plot Events into Sentences

Ammanabrolu, Prithviraj; Tien, Ethan; Cheung, Wesley; Luo, Zhaochen; Ma, William; Martin, Lara J.; Riedl, Mark O.

doi:10.1609/aaai.v34i05.6232

Cited by 47 publications

(54 citation statements)

References 15 publications

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“…However, researchers realize that word-by-word generation models cannot efficiently model the long dependency across sentences (See et al, 2019). Models using intermediate representations as guidance to generate stories are then proposed (Yao et al, 2019;Martin et al, 2018;Ammanabrolu et al, 2020;Fan et al, 2019;. These works are developed toward short stories and thus are insufficient for modeling novels (See Section 1).…”

Section: Related Workmentioning

confidence: 99%

Semantic Frame Forecast

Huang¹,

Huang²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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This paper introduces semantic frame forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the task's feasibility. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans. Our code

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Section: Related Workmentioning

confidence: 99%

Semantic Frame Forecast

Huang¹,

Huang²

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…To make a long story more coherent, recent work proposes to generate a skeleton and then generate the full text guided by the skeleton. The skeleton could be a sequence of SRL frames (Fan et al, 2019), a sequence of event structure (subject, verb, object, preposition, modifier) (Ammanabrolu et al, 2020), a story premise (Fan et al, 2018), or a story summary (Chen et al, 2019). Users can revise the skeleton to control the generated text, but the approaches assume the existence of the skeleton extractor or labels in the training corpus.…”

Section: Related Workmentioning

confidence: 99%

Changing the Mind of Transformers for Topically-Controllable Language Generation

Chang

Yuan²,

Iyyer

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Large Transformer-based language models can aid human authors by suggesting plausible continuations of text written so far. However, current interactive writing assistants do not allow authors to guide text generation in desired topical directions. To address this limitation, we design a framework that displays multiple candidate upcoming topics, of which a user can select a subset to guide the generation. Our framework consists of two components: (1) a method that produces a set of candidate topics by predicting the centers of word clusters in the possible continuations, and ( 2) a text generation model whose output adheres to the chosen topics. The training of both components is self-supervised, using only unlabeled text. Our experiments demonstrate that our topic options are better than those of standard clustering approaches, and our framework often generates fluent sentences related to the chosen topics, as judged by automated metrics and crowdsourced workers.

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“…If we add the phrase 'on the first day of the 18th century', the prediction becomes card, which matches the time setting. Ammanabrolu et al (2019) proposed an ensemble-based model to generate sentences given plot events. This involves two steps.…”

Section: Modelsmentioning

confidence: 99%

“…The next step is to expand these events to a story. We plan to experiment with the ensemble model by Ammanabrolu et al (2019) which is reported to combine the strength of the retrieve-and-edit method (Hashimoto et al, 2018), the template filling method, the sequence-to-sequence methods with finite state machine decoder, Monte Carlo beam decoding, and vanilla beam-decoding respectively. This method will conduct an event-to-event generation first to include more events before generating the output story.…”

Section: Modelsmentioning

confidence: 99%

Story-level Text Style Transfer: A Proposal

Qian¹

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Text style transfer aims to change the style of the input text to the target style while preserving the content to some extent. Previous works on this task are on the sentence level. We aim to work on story-level text style transfer to generate stories that preserve the plot of the input story while exhibiting a strong target style. The challenge in this task compared to previous work is that the structure of the input story, consisting of leading roles and their relations with each other, needs to be preserved, and that the generated story needs to be consistent after adding flavors. We plan to explore three methods including the BERT-based method, the Story Realization method, and the Graph-based method.

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Story Realization: Expanding Plot Events into Sentences

Cited by 47 publications

References 15 publications

Semantic Frame Forecast

Semantic Frame Forecast

Changing the Mind of Transformers for Topically-Controllable Language Generation

Story-level Text Style Transfer: A Proposal

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