<?xml version="1.0" standalone="yes"?>
<Paper uid="W04-0208">
  <Title>Temporal Discourse Models for Narrative Structure</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Getting a machine to understand human narratives has been a classic challenge for NLP and AI.</Paragraph>
    <Paragraph position="1"> Central to all narratives is the notion of time and the unfolding of events. When we understand a story, in addition to understanding other aspects such as plot, characters, goals, etc., we are able to understand the order of happening of events. A given text may have multiple stories; when we understand such a text, we are able to tease apart these distinct stories. Thus, understanding the story from a text involves building a global model of the sequences of events in the text, as well as the structure of nested stories. We refer to such models as Temporal Discourse Models (TDMs).</Paragraph>
    <Paragraph position="2"> Currently, while we have informal descriptions of the structure of narratives, e.g., (Bell 1999), we lack a precise understanding of this aspect of discourse. What sorts of structural configurations are observed? What formal characteristics do they have? For syntactic processing of natural languages, we have, arguably, answers to similar questions. However, for discourse, we have hardly begun to ask the questions.</Paragraph>
    <Paragraph position="3"> One of the problems here is that most of the information about narrative structure is implicit in the text. Thus, while linguistic information in the form of tense, aspect, temporal adverbials and discourse markers is often present, people use commonsense knowledge to fill in information.</Paragraph>
    <Paragraph position="4"> Consider a simple discourse: Yesterday Holly was running a marathon when she twisted her ankle.</Paragraph>
    <Paragraph position="5"> David had pushed her. Here, aspectual information indicates that the twisting occurred during the running, while tense suggests that the pushing occurs before the twisting. Commonsense knowledge also suggests that the pushing caused the twisting.</Paragraph>
    <Paragraph position="6"> We can see that even for interpreting such relatively simple discourses, a system might require a variety of sources of linguistic knowledge, including knowledge of tense, aspect, temporal adverbials, discourse relations, as well as background knowledge. Of course, other inferences are clearly possible, e.g., that the running stopped after the twisting, but when viewed as defaults, these latter inferences seem to be more easily violated. The need for commonsense inferences has motivated computational approaches that are domainspecific, using hand-coded knowledge (e.g., Asher and Lascarides 2003, Hitzeman et al. 1995).</Paragraph>
    <Paragraph position="7"> A number of theories have postulated the existence of various discourse relations that relate elements in the text to produce a global model of discourse, e.g., (Mann and Thompson 1988), (Hobbs 1985), (Hovy 1990) and others. In RST (Mann and Thompson 1988), (Marcu 2000), these relations are ultimately between semantic elements corresponding to discourse units that can be simple sentences or clauses as well as entire discourses. In SDRT (Asher and Lascarides 2003), these relations are between representations of propositional content, called Discourse Representation Structures (Kamp and Reyle, 1993).</Paragraph>
    <Paragraph position="8"> Despite a considerable amount of very productive research, annotating such discourse relations has proved problematic. This is due to the fact that discourse markers may be absent (i.e., implicit) or ambiguous; but more importantly, because in many cases the precise nature of these discourse relations is unclear. Although (Marcu et In addition to T1, we also have the temporal ordering constraints C1: {Eb &lt; Ec, Ec &lt; Ea, Ea &lt; Ed}. These are represented separately from the tree. A TDM is thus a pairing of tree structures and temporal constraints. More precisely, a Temporal Discourse Model for a text is a pair &lt;T, C&gt;, where T is a rooted, unordered, directed tree with nodes N = E [?] A, where E is the set of events mentioned in the text and A is a set of abstract events, and a parent-child ordering relation, [?] (temporal inclusion). A non-leaf node can be textually mentioned or abstract. Nodes also have a set of atomic-valued features. Note that the tree is temporally unordered left to right. C is a set of temporal ordering constraints using the ordering relation, &lt; (temporal precedence) as well as (for states, clarified below) 'minimal restrictions' on the above temporal inclusion relation (expressed as a [?] min ).</Paragraph>
    <Paragraph position="9"> al. 1999) (Carlson et al. 2001) reported relatively high levels of inter-annotator agreement, this was based on an annotation procedure where the annotators were allowed to iteratively revise the instructions based on joint discussion.</Paragraph>
    <Paragraph position="10"> While we appreciate the importance of representing rhetorical relations in order to carry out temporal inferences about event ordering, we believe that there are substantial advantages in isolating the temporal aspects and modeling them separately as TDMs. This greatly simplifies the representation, which we discuss next.</Paragraph>
  </Section>
class="xml-element"></Paper>