In this work, we present Web-STAR, an online platform for story understanding built on top of the STAR (STory comprehension through ARgumentation) reasoning engine. This platform includes a web-based IDE, integration with the STAR system and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features like public story sharing with a built-in commenting system, a public repository for sharing stories with the community and collaboration tools that can be used from both project team members for development and educators for teaching. Moreover, we discuss the ongoing work on adding new features and functionality to this platform.
Currently, several systems have been developed to deal with the problem of story comprehension. Starting from the 70's, Charniak presented two systems; a story comprehension model for answering questions about children stories, by relating stories to real-world background knowledge @cite_18 and a system for answering questions about simple stories dealing with painting @cite_22 . There is also work on Deep Read @cite_28 , an automated reading comprehension system that accepts stories and answers questions about them. More recent attempts, include work of Mueller @cite_7 , on a system for modeling space and time in narratives about restaurants and Genesis @cite_11 , a system for modeling and exploring aspects of story understanding using stories drawn from sources ranging from fairy tales to Shakespeare's plays.
@cite_18 @cite_22 @cite_7 @cite_28 @cite_11
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This paper describes initial work on Deep Read, an automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have acquired a corpus of 60 development and 60 test stories of 3rd to 6th grade material; each story is followed by short-answer questions (an answer key was also provided). We used these to construct and evaluate a baseline system that uses pattern matching (bag-of-words) techniques augmented with additional automated linguistic processing (stemming, name identification, semantic class identification, and pronoun resolution). This simple system retrieves the sentence containing the answer 30--40 of the time. Historically, story understanding systems have depended on a great deal of hand-crafted knowledge. Natural language understanding systems that use conceptual knowledge structures [SA77, Cul78, Wil78, Car79, Leh81, Kol83] typically rely on enormous amounts of manual knowledge engineering. While much of the work on conceptual knowledge structures has been hailed as pioneering research in cognitive modeling and narrative understanding, from a practical perspective it has also been viewed with skepticism because of the underlying knowledge engineering bottleneck. The thought of building a large-scale conceptual natural language processing (NLP) system that can understand open-ended text is daunting even to the most ardent enthusiasts. So must we grit our collective teeth and assume that story understanding will be limited to prototype systems in the foreseeable future? Or will conceptual natural language processing ultimately depend on a massive, broad-scale manual knowledge engineering effort, such as CYC [LPS86]? We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one. We have developed a rule-based system, Quarc, that can read a short story and find the sentence in the story that best answers a given question. Quarc uses heuristic rules that look for lexical and semantic clues in the question and the story. We have tested Quarc on reading comprehension tests typically given to children in grades 3--6. Overall, Quarc found the correct sentence 40 of the time, which is encouraging given the simplicity of its rules.