Improving Fluency in Narrative Text Generation With Grammatical Transformations and Probabilistic Parsing

In research on automatic generation of narrative text, story events are often formally represented as a causal graph. When serializing and realizing this causal graph as natural language text, simple approaches produce cumbersome sentences with repetitive syntactic structure, e.g. long chains of “because” clauses. In our research, we show that the fluency of narrative text generated from causal graphs can be improved by applying rule-based grammatical transformations to generate many sentence variations with equivalent semantics, then selecting the variation that has the highest probability using a probabilistic syntactic parser. We evaluate our approach by generating narrative text from causal graphs that encode 100 brief stories involving the same three characters, based on a classic film of experimental social psychology. Crowdsourced workers judged the writing quality of texts generated with ranked transformations as significantly higher than those without, and not significantly lower than human-authored narratives of the same situations. 1 Narrative Text Generation Across several academic disciplines, it has become common to represent narratives as causal graphs. In the causal network model of psychologists Trabasso and van den Broek (1985), vertices in the graph structure represent settings, events, goals, attempts and outcomes of a narrative, linked via directed edges that encode cause/effect relationships. In computer science, similar causal graphs have been used to model and manipulate narrative elements including suspense (Cheong and Young, 2014), conflict (Ware and Young, 2011), flashback and foreshadowing (Bae and Young, 2008). Elson (2012) elaborates the causal network model by relating it to both the temporal ordering of story-world events and an author’s textual realization, creating a three-layer Story Intention Graph. Causal graph representations of narrative create new opportunities for natural language generation (NLG) of narrative text. For example, Lukin et al. (2015) describe a narrative NLG pipeline for Story Intention Graphs, generating variations of an original text that can be parameterized for particular discourse goals. When serializing and realizing a causal graph structure as natural language text, some care must be taken to avoid the generation of cumbersome sentences with repetitive syntactic structure, e.g. as a long chain of “because” clauses. Lukin et al. (2015) directly compared readers’ overall preferences for certain causal connectives over others, finding that no single class of variations will produce sentences that are preferable to a human author’s stylistic choices. We hypothesize that the policies used by native speakers to select among lexical-syntactic variations are complex and content-dependent, and are best described in statistical models trained on natural language corpora. In this paper, we explore a new approach to narrative NLG that integrates rule-based and statistical methods to produce fluent realizations of storylines encoded as causal graphs. Beginning with the output of a simple baseline system, we show that the fluency of narrative text generated