Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events. Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain. In this paper, we explore open-domain story generation that writes stories given a title (topic) as input. We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline. We compare two planning strategies. The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories. Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.

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In this paper, we analyze the performance of name finding in the context of a variety of automatic speech recognition (ASR) systems and in the context of one optical character recognition (OCR) system. We explore the effects of word error rate from ASR and OCR, performance as a function of the amount of training data, and for speech, the effect of out-of-vocabulary errors and the loss of punctuation and mixed case I

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Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via highquality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle's Poetics. We find that stories written with our more principled plotstructure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way. 1 Prompt: Write the most beautiful end to the universe that you can imagine Naive Plot with Semantic Role Labelling Tags: The universe end ent 0 see ent 3 dying ent 1 # ent 1 left ent 0 the last human live ent 6 end # ent 2 come the last one die a universe of life left Revised Plot with Semantic Role Labelling Tags: The light filled the sky A bright flash lit # began to grow # the stars grow began ent 0 to fade # The stars fade ent 2 looked ent 1 me dying star Naive Generated Story: "You're dead. I'll be dead, and there'll always be no more of you."* *"No, my dear. It's not true!" -The last of the world is dead, * * "It'll never be over. The universe will n't end. Not yet. But I'll see it, one way or another. There is no other way."* *My last thought, the dying man left me. He told that I was the last human to live. And he told me the universe would end, and the end will come soon. That your death would be the last one to die, and the first one to be the most beautiful. * "There is a universe of life left, my dear, and that's all there is. Generated Story (from revision): It was the end of the universe. The light filled the sky. The galaxy. All the light in all the worlds, and all the stars. A bright flash. The first light. And then a bright flash of light... then a flash of darkness. Then a bright light. A flash of blackness and then the stars began to grow. It was a bright, beautiful bright, light. And then, slowly the stars started to grow in number. There were millions and millions of stars.. But, slowly but surely, all the galaxies began to fade. Slowly, but surely. Finally, it looked at me with its eyes. One dying star. Gold Story: It slept, and dreamt of twirling lights. It cradled countless twirling lights, all dancing in their flashing ways, dancing to the sound of a faraway flute. The light was vibrant and warm and spun all around It. The tiny lights grew with the tune, and more lights came to dance within the luminescence. It was surrounded by light, all waltzing in their ways t...

SummaryInformation retrieval (IR)

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We propose a novel string-to-dependency algorithm for statistical machine translation. This algorithm employs a target dependency language model during decoding to exploit long distance word relations, which cannot be modeled with a traditional n-gram language model. Experiments show that the algorithm achieves significant improvement in MT performance over a state-of-the-art hierarchical string-to-string system on NIST MT06 and MT08 newswire evaluation sets.