SECTION 4 HIGHLIGHTS THE MANAGEMENT OF THE INTER-FORMANCE OF A SYSTEM...

. The causality implied by the

entities. In this case, four types of reference are

answer from Figure 5 has two components: (1)

used to associate these linguistic objects with

the effect (i.e. the predicate

⁸

) and (2) the re-

sult, which eliminates the semantic effect of the

other related objects: (a) definitional reference,

used to link entity (E1) “Anwar Sadat” to a cor-

negative polarity item never by implying the

responding attribute “Egyptian President”, (b)

predicate

^!

, obstacle. The questions that are

metonymic reference, since (E1) can be coerced

generated are based on question patterns asso-

into (E2), (c) part-whole reference, since “BW

ciated with causal relations and therefore allow

stockpiles”(E4) necessarily imply the existence

different degrees for the specificity of the resul-

of a “BW program”(E5), and (d) relational ref-

tative, i.e obstacle or deterrent.

erence, since validating is subsumed as part

We generated several questions for each answer

of the meaning of declaring (as determined by

passage. Questions were generated based on pat-

WordNet glosses), while admitting can be de-

terns that were acquired to model interrogations

fined in terms of declaring, as in declaring [to

using relations between predicates and their argu-

be true].

ments. Such interrogations are based on (1) as-

ANSWER

sociations between the answer type (e.g. DATE)

Egyptian Deputy Minister Mahmud Salim states that Egypt’s

enemies would never use BW because they are aware that the

and the question stem (e.g. “when” and (2) the

Egyptians have "adequate means of retaliating without delay".

Predicates: P’1=state; P’2 = never use; P3 = be aware;

relation between predicates, question stem and the

P’4 = have P"4 = "the possesion"

Causality:

words that determine the answer type (Narayanan

P"4 = "the possesion" = nominalization(P’4) = EFFECT(P’2(BW))

and Harabagiu, 2004). In order to obtain these

P’2(BW) = NON−NEGATIVE RESULT(P5); P’5 = "obstacle"

PROCESSING

specialization

Reference: P’1 P’6 = view

predicate-argument patterns, we used 30% (approxi-

mately 1500 questions) of the handcrafted question-

Pattern: Does Egypt P’6 P"4(BW) as a P’5?

Does Egypt view the possesion of BW as an obstacle?

answer pairs, selected at random from each of the 8

Does Egypt view the possesion of BW as a deterrent?

dialogue scenarios. As Figures 4 and 5 illustrate, we

Figure 5: Questions for Implied Causal Relations.

used patterns based on (a) embedded predicates and

Problem 2: We have found that the identifica-

(b) causal or counterfactual predicates.

tion of the association between a candidate an-

swer and a question depends on (a) the recogni-

4 Managing Interactive Q/A Dialogues

tion of predicates and entities based on both the

output of a named entity recognizer and a se-

As illustrated in Figure 1, the main idea of man-

mantic parser (Surdeanu et al., 2003) and their

aging dialogues in which interactions with the Q/A

system occur is based on the notion of predictions,

structuring into predicate-argument frames, (b)

i.e. by proposing to the user a small set of questions

the resolution of reference (addressed in Prob-

that tackle the same subject as her question (as illus-

lem 1), (c) the recognition of implicit rela-

trated in Table 1). The advantage is that the user can

tions between predications stated in the answer.

follow-up with one of the pre-processed questions,

Some of these implicit relations are referential,

8

8

<

as is the relation between predicates

and

that has a correct answer and resides in one of the

QUABs. This enhances the effectiveness of the dia-

illustrated in Figure 4. A special case of im-

logue. It also may impact on the efficiency, i.e. the

plicit relations are the causal relations. Fig-

ure 5 illustrates an answer where a causal re-

number of questions being asked if the QUABs have

lation exists and is marked by the cue phrase

good coverage of the subject areas of the scenario.

because. Predicates – like those in Figure 5 –

Moreover, complex questions, that generally are not

) or negative (like

can be phrasal (like

processed with high accuracy by current state-of-

the-art Q/A systems, are associated with predictive

and

⁸

’ as they are the ones that ultimately de-

questions that represent decompositions based on

similarities between predicates and arguments of the

as

^,)

^:

@

A

CB

original question and the predicted questions.

=

D

<

:E

:>FN

%

E

The selection of the questions from the QUABs

6

F