What is the role of textual features above the sentence level in advancing the machine translation of literature? This paper examines how referential cohesion is expressed in literary and non-literary texts and how this cohesion affects translation. We first show in a corpus study on English that literary texts use more dense reference chains to express greater referential cohesion than news. We then compare the referential cohesion of machine versus human translations of Chinese literature and news. While human translators capture the greater referential cohesion of literature, Google translations perform less well at capturing literary cohesion. Our results suggest that incorporating discourse features above the sentence level is an important direction for MT research if it is to be applied to literature.

This thesis describes new approaches to the formal modeling of narrative discourse. Although narratives of all kinds are ubiquitous in daily life, contemporary text processing techniques typically do not leverage the aspects that separate narrative from expository discourse. We describe two approaches to the problem. The first approach considers the conversational networks to be found in literary fiction as a key aspect of discourse coherence; by isolating and analyzing these networks, we are able to comment on longstanding literary theories. The second approach proposes a new set of discourse relations that are specific to narrative. By focusing on certain key aspects, such as agentive characters, goals, plans, beliefs, and time, these relations represent a theory-of-mind interpretation of a text. We show that these discourse relations are expressive, formal, robust, and through the use of a software system, amenable to corpus collection projects through the use of trained annotators. We have procured and released a collection of over 100 encodings, covering a set of fables as well as longer texts including literary fiction and epic poetry. We are able to inferentially find similarities and analogies between encoded stories based on the proposed relations, and an evaluation of this technique shows that human raters prefer such