SECTION A BENEFIT, SINCE IT GIVES MORE OPPORTUNITY FOR ENFORC-
3.4 Profiting From Inversions
As shown in the previous section, not all questions
Broadly speaking, our goal is to keep or re-rank the
have easily generated inverted forms (even by a hu-
candidate answer hit-list on account of inversion
man). However, we do not need to explicate the
results. Suppose that a question Q is inverted
inverted form in natural language in order to process
around pivot term T, and for each candidate answer
the inverted question.
C
i
, a list of “inverted” answers {C
ij
} is generated as
described in the previous section. If T is on one of
In our system, a question is processed by the
the {C
ij
}, then we say that C
i
is validated. Valida-
tion is not a guarantee of keeping or improving C
i
’s
Q
UESTIONP
ROCESSINGmodule, which produces a
position or score, but it helps. Most cases of failure
structure called a QFrame, which is used by the sub-
to validate are called refutation; similarly, refutation
sequent S
EARCHand A
NSWERS
ELECTIONmodules.
The QFrame contains the list of terms and phrases in
of C
i
is not a guarantee of lowering its score or posi-
the question, along with their properties, such as
tion.
POS and NE-type (if it exists), and a list of syntactic
relationship tuples. When we have a candidate an-
It is an open question how to adjust the results of the
swer in hand, we do not need to produce the inverted
initial candidate answer list in light of the results of
English question, but merely the QFrame that would
the inversion. If the scores associated with candi-
have been generated from it. Figure 1 shows that
date answers (in both directions) were true prob-
the C
ONSTRAINTSM
ODULEtakes the QFrame as one
abilities, then a Bayesian approach would be easy to
develop. However, they are not in our system. In
of its inputs, as shown by the link from QP in QS1
addition, there are quite a few parameters that de-
to CM. This inverted QFrame can be generated by a
scribe the inversion scenario.
set of simple transformations, substituting the pivot
term in the bag of words with a candidate answer
Suppose Q generates a list of the top-N candidates
<C
ANDA
NS>, the original answer type with the type
{C
i
}, with scores {S
i
}. If this inversion method
of the pivot term, and in the relationships the pivot
were not to be used, the top candidate on this list,
term with its type and the original answer type with
<C
ANDA
NS>. When relationships are evaluated, a
C
1
, would be the emitted answer. The question gen-
type token will match any instance of that type. Fig-
erated by inverting about T and substituting C
i
is
ure 2 shows a simplified view of the original
QT
i
. The system is fixed to find the top 10 passages
QFrame for “What was the capital of Germany in
responsive to QT
i
, and generates an ordered list C
ij
1945?”, and Figure 3 shows the corresponding In-
of candidate answers found in this set.
verted QFrame. C
OUNTRYis determined to be a
better type to invert than Y
EAR, so “Germany” be-
Each inverted question QT
i
is run through our sys-
comes the pivot. In Figure 3, the token
tem, generating inverted answers {C
ij
}, with scores
<C
ANDA
NS> might take in turn “Berlin”, “Mos-
{S
ij
}, and whether and where the pivot term T shows
cow”, “Prague” etc.
up on this list, represented by a list of positions {P
i
},
where P
i
is defined as:
Keywords: {1945, Germany, capital}
AnswerType: C
APITALP
i
= j if C
ij
= T, for some j
P
i
= -1 otherwise
Relationships: {(Germany, capital), (capital,
C
APITAL), (capital, 1945)}
We added to the candidate list the special answer
nil, representing “no answer exists in the corpus.”
Figure 2. Simplified QFrame
As described earlier, we had observed from training
Keywords: {1945, <C
ANDA
NS>, capital}
data that failure to validate candidates of certain
AnswerType: C
OUNTRYtypes (such as Person) would not necessarily be a
Relationships: {(C
OUNTRY, capital), (capital,
real refutation, so we established a set of types
<C
ANDA
NS>), (capital, 1945)}
SOFTR
EFUTATIONwhich would contain the broadest
of our types. At the other end of the spectrum, we
Figure 3. Simplified Inverted QFrame.
observed that certain narrow candidate types such as
UsState would definitely be refuted if validation
The output of QS2 after processing the inverted
didn’t occur. These are put in set
MUSTC
ONSTRAIN.
QFrame is a list of answers to the inverted question,
Our goal was to develop an algorithm for recomput-
which by extension of the nomenclature we call “in-
ing all the original scores {S
i
} from some combina-
verted answers.” If no term in the question has an
tion (based on either arithmetic or decision-trees) of
identifiable type, inversion is not possible.
{S
i
} and {S
ij
} and membership of
SOFTR
EFUTATIONo P
i
was the rank of the validating answer to ques-
and
MUSTC
ONSTRAIN. Reliably learning all those
tion QT
i
o A
i
was the score of the validating answer to QT
i