372 research outputs found
Centering, Anaphora Resolution, and Discourse Structure
Centering was formulated as a model of the relationship between attentional
state, the form of referring expressions, and the coherence of an utterance
within a discourse segment (Grosz, Joshi and Weinstein, 1986; Grosz, Joshi and
Weinstein, 1995). In this chapter, I argue that the restriction of centering to
operating within a discourse segment should be abandoned in order to integrate
centering with a model of global discourse structure. The within-segment
restriction causes three problems. The first problem is that centers are often
continued over discourse segment boundaries with pronominal referring
expressions whose form is identical to those that occur within a discourse
segment. The second problem is that recent work has shown that listeners
perceive segment boundaries at various levels of granularity. If centering
models a universal processing phenomenon, it is implausible that each listener
is using a different centering algorithm.The third issue is that even for
utterances within a discourse segment, there are strong contrasts between
utterances whose adjacent utterance within a segment is hierarchically recent
and those whose adjacent utterance within a segment is linearly recent. This
chapter argues that these problems can be eliminated by replacing Grosz and
Sidner's stack model of attentional state with an alternate model, the cache
model. I show how the cache model is easily integrated with the centering
algorithm, and provide several types of data from naturally occurring
discourses that support the proposed integrated model. Future work should
provide additional support for these claims with an examination of a larger
corpus of naturally occurring discourses.Comment: 35 pages, uses elsart12, lingmacros, named, psfi
Limited Attention and Discourse Structure
This squib examines the role of limited attention in a theory of discourse
structure and proposes a model of attentional state that relates current
hierarchical theories of discourse structure to empirical evidence about human
discourse processing capabilities. First, I present examples that are not
predicted by Grosz and Sidner's stack model of attentional state. Then I
consider an alternative model of attentional state, the cache model, which
accounts for the examples, and which makes particular processing predictions.
Finally I suggest a number of ways that future research could distinguish the
predictions of the cache model and the stack model.Comment: 9 pages, uses twoside,cl,lingmacro
Inferring Acceptance and Rejection in Dialogue by Default Rules of Inference
This paper discusses the processes by which conversants in a dialogue can
infer whether their assertions and proposals have been accepted or rejected by
their conversational partners. It expands on previous work by showing that
logical consistency is a necessary indicator of acceptance, but that it is not
sufficient, and that logical inconsistency is sufficient as an indicator of
rejection, but it is not necessary. I show how conversants can use information
structure and prosody as well as logical reasoning in distinguishing between
acceptances and logically consistent rejections, and relate this work to
previous work on implicature and default reasoning by introducing three new
classes of rejection: {\sc implicature rejections}, {\sc epistemic rejections}
and {\sc deliberation rejections}. I show how these rejections are inferred as
a result of default inferences, which, by other analyses, would have been
blocked by the context. In order to account for these facts, I propose a model
of the common ground that allows these default inferences to go through, and
show how the model, originally proposed to account for the various forms of
acceptance, can also model all types of rejection.Comment: 37 pages, uses fullpage, lingmacros, name
Inferring Narrative Causality between Event Pairs in Films
To understand narrative, humans draw inferences about the underlying
relations between narrative events. Cognitive theories of narrative
understanding define these inferences as four different types of causality,
that include pairs of events A, B where A physically causes B (X drop, X
break), to pairs of events where A causes emotional state B (Y saw X, Y felt
fear). Previous work on learning narrative relations from text has either
focused on "strict" physical causality, or has been vague about what relation
is being learned. This paper learns pairs of causal events from a corpus of
film scene descriptions which are action rich and tend to be told in
chronological order. We show that event pairs induced using our methods are of
high quality and are judged to have a stronger causal relation than event pairs
from Rel-grams
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