Submitted by
Assigned_Reviewer_7
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
The paper proposes a matrix completion approach to the
cross domain classification task, which is capable of exploiting labeled
data in an auxiliary domain and also unlabeled parellel data. The approach
involves two steps. First, it constructs a single incomplete matrix that
includes all documents and domains, and then completes this matrix while
enforcing lowrank and sparsity conditions using the projected gradient
descent algorithm. Second, it reduces the feature dimension of this
completed matrix using LSI, and then trains a standard classifier on this
new representation. The paper also presents a convergence guarantee on the
projected gradient descent algorithm, and favorable experimental results.
Here are some comments and questions:
 After estimating
the completed matrix M*, you reduce the feature dimension from d to k with
LSI because M* "lacks sufficient capacity of handling feature sparseness".
Can you elaborate on this? Is it basically that you want small dimensions
for computational convenience, since M* will be relatively dense even with
the sparsity enforcement? Also, in that case, is there a reason you chose
LSI over other dimensionality reduction techniques (e.g., PCA)?

There seem to be two disjoint aspects of exploiting external data in this
paper: 1. leveraging the labeled source language data to help learn the
labeled target language data, and 2. leveraging the unlabeled parallel
data. If this is correct, it would be helpful to clearly distinguish the
two and report results separately.
 On a related note, the
experiment settings are a bit unclear (section 5.2). So you were using (a)
4k labeled source language documents, (b) 100 labeled target language
documents, and (c) 2k unlabeled parallel documents for your training data?
And you are reporting the classification results on the remaining 1900
labeled target language documents? Then the only difference in setion 5.3
is that you are varying the number of unlabeled parallel documents from
200 to 2k? In that case, why is the performance of CLKCCA better than TSL
in Figure 1 for EFM and EGM at 2k but it is not in Table 1?
 Can
you briefly clarify why a fully observed documentterm matrix would be
lowrank? It is sparse, but probably no document is a linear combination
of other documents in vocabulary, meaning the rank in that case will be
min(d, k).
 Can you give an intuition on why TSL is able to
perform better in general than other methods, especially CCA? A multiview
approach like CCA feels like a natural approach to this cross language
representation learning task. In contrast, the matrix completion approach
is not as natural. It seems the main driving force is enforcing the
lowrank condition on M*. How does this compare with other techniques?
 What is the running time of the algorithm? Does the projected
gradient descent algorithm take more, less, or about the same time as
other methods like CLLSI, CLKCCA, and CLOPCA?
Here are some
suggested changes:
 Fix (ij) to (i,j) two sentences before
equation (2).  Remove "such as" in the sentences before equation (4)
and (5). Q2: Please summarize your review in 12
sentences
The paper proposes a matrix completion approach to the
cross domain classification task. Although some technical points in the
paper need to be clarified, the method is simple, effective, and in
general clearly presented. Submitted by
Assigned_Reviewer_8
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
Summary: This paper presents a twostep method to
learn a crosslingual topic representation using matrix completion and
latent semantic analysis techniques. It uses a projected gradient descent
algorithm to optimize the matrix completion problem. Experimental results
show that the proposed method outperforms monolingual baseline and
crosslingual baselines on sentiment analysis task on parallel Amazon
review dataset. Learning curves with different sizes of unlabeled data
illustrate that the proposed algorithm learns highly accurate
crosslingual topic representation with relatively small number of
parallel data.
Quality: Experiments were thoroughly carried out to
support the claim. The propose twostep learning algorithm is carefully
compared with two baselines (plain bagofwords, cross lingual LSA) and
two stateoftheart crosslingual dimensionality reduction algorithms.
Clarity: This paper is wellorganized. All the parameters
(including how to select them) were written in the paper; it is easy to
reproduce the result. Just a minor comment: I would suggest explaining the
motivation of using matrix completion more in Introduction (though it is
described in Section 3).
Originality: Although the task of
crosslingual representation learning is not new, the application of
matrix completion to this task is novel and it is clear that the proposed
algorithm achieves better performance than previous methods in the
literature.
Significance: The result of the twostep approach is
consistently higher than other baselines. We can learn form this work that
matrix completion can be used as a preprocessing for crosslingual
sentiment classification task, and this kind of formulation may be
effective in other tasks as well. Q2: Please summarize
your review in 12 sentences
This paper proposes a twostep method to learn a
crosslingual topic representation which perform matrix completion before
latent semantic analysis on crosslingual sentiment classification task.
Experimental results show that the proposed method outperforms
monolingual baseline and other crosslingual baselines including one of
the stateoftheart methods, and is stable even when trained on different
sizes of unlabeled parallel data. Submitted by
Assigned_Reviewer_9
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
The paper proposes a method for translingual
representation learning by first using matrix completion to create
multilingual vectors for each row, and then performing CLLSI on the
multilingual vectors.
Quality: While this seems like a good idea,
I'm not sure that the results are robust. The positive results could
simply be caused by the scaling of LSI being better than OPCA or CCA for a
linear SVM.
I would urge the authors to use an ML system that is
insensitive to rescalings of the individual feature: for example, boosted
decision trees (available in Weka). If the boosted decision trees on top
of the twostep system works better than other techniques, then I believe
the result.
Significance: If this is really a robust result, it
would be neat, because matrix completion is a wellcontrolled nonlinear
inference technique.
Novelty: The idea seems new.
Clarity:
There were some issues of clarity in the paper. Line 113 talks about
"construct[ing] a unified documentterm matrix for all documents". I'm not
sure whether this means using one term for every surface form (like
"Merkel"), or labeling each surface form from each language (like
"Merkel_en" and "Merkel_de"). Please clarify. Also, the description of the
experimental setup is split across Table 1, so it took me a little while
to figure it out.
Q2: Please summarize your review
in 12 sentences
Seems like a neat idea. I wish that the authors tried
other classifiers than linear SVM  I just don't know whether the result
is robust, or a scaling artifact.
Q1:Author
rebuttal: Please respond to any concerns raised in the reviews. There are
no constraints on how you want to argue your case, except for the fact
that your text should be limited to a maximum of 6000 characters. Note
however that reviewers and area chairs are very busy and may not read long
vague rebuttals. It is in your own interest to be concise and to the
point.
