Tadahiro KITAHASHI, Noboru BABAGUCHI, and Atsuko TAKANO
The Institute of Scientific and Industrial Research, Osaka University
8-1 Mihogaoka, Ibaraki 567 Japan
Generation of cooperative answers is one of the important
issues for realization of natural language interface
system. Last year we reported an overall model of
recognizing diversified patterns of Question \& Answer
pairs based on presuppositions underlying questions, which
have been regarded to be essential in many studies on
cooperative answers. This year we have been pursuing an
application of the model to generation of cooperative
answers.
In a similar way of formalizing diversified patterns of
Q-A pairs, we formalize the process of generating
cooperative answers based on the following two points.
One is speaker's misunderstanding of the hearer's situation
that is detected from discrepancy or
contradiction in the presuppositions under questions. In
such cases, the replies may point out the
misunderstandings or may search answers satisfying the
intentions of them. The other is consideration about some
background aiming at helping
the speaker understand the situation clearly. In the process
of formalizing generation of answers, we have
realized that there is a pattern of answers which is unable to be
generated by an algorithmic procedure unlike other answer patterns.
We name the case as 'suggestions for speaker's creating answer',
One of the pragmatic presupposes at speaker's inquiry to a hearer
is that the hearer has enough knowledge for answering the question.
(In this paper, it is assumed that
a speaker asks a question and a hearer replies.)
The above case arises from the contradiction of the
pragmatic presupposition. The 'suggestions for speaker's creating answer'
should be relevant to supporting the process of speaker's
inference for creating an answer to his own question. The following
utterance R1 is an example of the 'suggestive knowledge
for speaker's creating an answer' to the question Q1.
Q1: Kujira ha honyu-rui desuka.
(Is a whale mammal?)
R1: Kodomo ha umimasuyone.
(It breeds babies, doesn't it?)
R2 is another example of 'suggestions for speaker's creating an answer'
to Q2. Here the speaker may be able to infer a reply to his own
question Q2 by referring the hearer's utterance R2, who also has no idea
about the answer.
Q2: Sanzenin no sanpai kyaku ha hotondo jyosei desuka.
(Are most worshipers of Sanzenin women?)
R2: Jyakkouin no sanpai kyaku ha hotondo jyosei desu.
(Most worshipers of Jyakkouin are women.)
As before, it is considered to be difficult to figure out an
algorithmic procedure for generating such kind of answers. Then we
have taken an approach to preparing an association mechanism based on
an example-base of dialogues for replying questions at this kind of
situation.
We propose a model of generating answers based on the
association mechanism with an example-base of dialogues. We are
examining the flexibility of the method by
the experiment of retrieving data similar to key
cases from the database of old temples in Japan. Then, we are going to
consider about applying the mechanism to generation of
'suggestions for speaker's creating an answer'.
Our method firstly evaluates the viewpoint for a given
question and retrieves a Q-A pair whose question is
conceptually similar to a given question from the
example-base of dialogues based on the viewpoint. Then it modifies the
answer example utterance of the retrieved Q-A pair to
generate an answer appropriate to the given question in an
assumed situation.
In this method, semantic structures of utterances are
represented by an extended case structure. They are composed
of predicates, attributes of predicates and case elements.
Attributes of predicates represent modal information or
pragmatic information of the predicates. We consider
fourteen types of information as the values of the
attributes such as State, Possible, Cause, Guess and Past.
We are currently sorting out the type of cases as
follow;Agent, Object, Tense, Location, Tool, Degree, Cause
and Comparison. The following expressions are examples of
the semantic structure of question R3 and question R4.
R3:Hiroshima made donokuraide ikemasuka.
$<$(iku possible),((Location,("genzaichi",Hiroshima)),(Degree,HOW))$>$
R4:Hiko-ki to Shinkansen ha dochira ga toku desuka.
$<$(tokudearu state),((Object,(WHICH,Hiko-ki,Shinkansen)))$>$
Here, context processing problems such as anaphoric
reference and elliptical case elements are supposed to
have been resolved. The example-base of dialogues consists of
Q-A pairs, where utterances are represented in the form of
the semantic structures. Although the example-base has a flat
structure now, it should be systematized in future for
efficient reference.
The similarities between given questions and example
questions in the example-base of dialogues are evaluated by
the conceptual similarities between their predicates,
their attributes and their case elements each by each. In
order to define the measure of the similarity between
predicates, we are constructing the hierarchical tree of
predicates. The measure of two predicates is defined using
the distance between them on the tree. The similarities
between each two cases elements are also evaluated using
the distance between them on the hierarchical tree of
nouns.
We are now building the dialogue example base at the
information desk of sightseeing. We are going to test the
feasibility of our method by applying it to many sample
questions, in the situation where hearer does not have
enough knowledge for answering the question.