Review Comment:
The paper is now much improved and gives good detailed information
about what is done and how. There are due to the additions a host of
minor language problems that need to be fixed again: these are listed
below. There is also considerable repetition in the final review of
the state of the art which needs to be removed or completely rephrased
so that the same things are not said twice! For example, we have
virtually the same information on p. 2 and p.11 on Xser: this cannot
stay as it is. If the technical issues raised by the other reviewers
are now in their opinion well addressed and these problems are fixed,
then I'd support publication.
However, as a rather general point, and after testing out the provided link to
the web interface, I was left wondering about how the reliability of
the answer can be supported. For example, I asked the following the
question almost at random: 'how many countries are there in Europe?'
and got the answer 27. Now, 27 what? The UN lists 44 countries in
Europe, while the EU has 28 including the UK... seems that to make the
answers useful, one would need more than a bare figure. The SPARQL
indicates that what was being counted was
Now while this is clearly beyond the scope of the present paper, some
indications of how the system will support *explainability*, an
increasingly important property of intelligent systems, would add
value considerably. Perhaps further information about the measures
used would help here too. As the authors write:
"An interesting fact related to our approach is that each step we run
provides insights on how well it has been performed."
providing more of this information back to the user could then also be valuable.
It is also very easy to form questions for which no template is found,
so suggestions that the 7 defined are already substantial in their
coverage should probably be scaled down in comparison to a more
realistic measure of the kinds of ranges of questions that might
actually occur, as well as indications of the effort involved in
broadening coverage. Or give a stronger argument that the QALD
question catalogue is sufficient for broader application. I would
agree that the results are promising for the small set of templates,
but it is important not to oversell. The authors begin to address this
in the discussion:
"but also that specific patterns may support
only few questions."
and this needs to be given due attention to convince that the method
is scalable in a useful way. Reference here could possibly be made to
other work working on learning connections between semantic
representations and natural language expressions of those semantics:
indeed, having a semantic representation of the questions might well
make the mapping to SPARQL easier, no? One would expect the use of
regular expressions at some point to come up against limitations...
Language corrections:
-------------------
on a tailored user interface, that require --> that requires
2015 QALD challenges [2] the respectively : no 'the' before 'respectively'
This suggests that translating natural language
questions into SPARQL queries is a really hard task. : hardly a surprise!
As we will see in the followings --> As we will see in the following
They value can be both --> Their (?) value can be both
city in which the measured spend happened : 'spend' is not a noun, do you mean 'expenditure'?
this has to be corrected whenever 'spend' appears.
attribute values they are also used : attribute values are also used ?
using Stanford tokenizer --> using the Stanford tokenizer
and the structure of dataset. --> and the structure of the dataset.
right dataset is peculiar of the statistical --> right dataset is peculiar to the statistical
defined in a datasets --> defined in a dataset
reduces of orders of magnitudes --> reduces by orders of magnitudes
thanks to the following trick: 'trick' is horribly informal and does not sound at all serious - I'd suggest phrasing more appropriately
we lookup the index for every n-grams --> we lookup the index for every n-gram
7-grams up to single words: *down* to single words surely
how much his city spent for public : do not use 'he' for generic reference!
the largest sum of spent amount of money. -> the largest sum of money spent.
namely the number 4, --> namely number 4,
our system need to manually --> our system needs to manually
can be also derived by --> can also be derived by
The online system allows to freely type questions -->
The online system allows the user to freely type questions
over the two set of questions : *sets*
The most performer in this comparison : *best* performer!
while givin up answering: *giving*
which enables to define grammars --> which enables the definition of
grammars
The adopted solution in literature --> The adopted solution in the
literature
Questions in the task 3 of QALD-6 testbed all refers : *refer*!!
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