Cutting Recursive Autoencoder Trees
Authors: Christian Scheible, Hinrich Schuetze
ICLR 2013

Abstract: Deep Learning models enjoy considerable success in Natural Language Processing. While deep architectures produce useful representations that lead to improvements in various tasks, they are often difficult to interpret. This makes the analysis of learned structures particularly difficult. We therefore have to rely on empirical tests to see whether a particular structure makes sense. In this paper, we present an analysis of a well-received model that produces structural representations of text: the Semi-Supervised Recursive Autoencoder. We show that for certain tasks, the structure of the autoencoder may be significantly reduced and we evaluate the produced structures through human judgment.

Cutting Recursive Autoencoder Trees
Christian Scheible
Institute for Natural Language Processing
University of Stuttgart, Germany
scheibcn@ims.uni-stuttgart.de
Hinrich Sch¨utze
Center for Information and Language Processing
University of Munich, Germany
Abstract
Deep Learning models enjoy considerable success in Natural Language Process-
ing. While deep architectures produce useful representations that lead to improve-
ments in various tasks, they are often difﬁcult to interpret. This makes the analy-
sis of learned structures particularly difﬁcult. In this paper, we rely on empirical
tests to see whether a particular structure makes sense. We present an analysis of
the Semi-Supervised Recursive Autoencoder, a well-known model that produces
structural representations of text. We show that for certain tasks, the structure of
the autoencoder can be signiﬁcantly reduced without loss of classiﬁcation accu-
racy and we evaluate the produced structures using human judgment.
1 Introduction
Deep Learning (DL) approaches are gaining more and more attention in Natural Language Pro-
cessing. Many NLP tasks have been addressed: syntactic parsing (Socher et al., 2010), semantic
role labeling (Collobert et al., 2011), machine translation (Deselaers et al., 2009), and document
classiﬁcation (Glorot et al., 2011).
One important issue in applying DL lies in the structure of language. DL originated in image pro-
cessing where the neighborhood of two entities, e.g. pixels, has a straightforward interpretation
through spatial relations. In NLP, neighborhood relations are not always interpretable easily and
don’t necessarily translate into a semantic relationship. Often, long distance dependencies hinder
the use of locally sensitive models.
A frequently cited DL model for NLP tasks is a model introduced by (Socher et al., 2011), the
Semi-Supervised Recursive Autoencoder (RAE). This model aims for a compositional approach,
representing each word as a vector and recursively applying an autoencoder function to unify pairs
of nodes, yielding a tree. One appealing property of RAEs is that the resulting structure lends itself
to a syntactic or even semantic interpretation. However, so far no in-depth analysis of RAEs in terms
of these structures has been performed.
Our main interest in this paper is to analyze the behavior of RAEs. We will investigate the following
key questions: (i) Are the structures produced by RAEs interpretable syntactically or semantically?
(ii) Can the structures be simpliﬁed?
We will analyze these issues empirically in a sentiment classiﬁcation task that RAE has previously
been used for (Socher et al., 2011). We introduce two methods for analysis. First, we try to simplify
the RAE structures automatically and evaluate the resulting models on a classiﬁcation task. Second,
we let humans rate the structures according to syntactic and semantic criteria.
1
arXiv:1301.2811v3  [cs.CL]  26 Apr 2013
In Section 2, we describe RAEs, particularly highlighting some details regarding implementation.
We then turn to different ways of structural simpliﬁcation in Section 3. Section 4 introduces the task
we use for evaluation. Section 5 contains error analysis of RAEs conducted by human annotators.
In Section 6 we carry out the experiments on structural simpliﬁcations.
2 Semi-Supervised Recursive Autoencoders
The central model in this paper is the Semi-Supervised Recursive Autoencoder (RAE) (Socher et al.,
2011). This section describes this model and discusses some important implementation details.
The RAE is a structural model. It recursively applies a neural network, the autoencoder, to construct
a tree structure over the words in a sentence. Each word is represented as a vector which is inde-
pendent of the context in which the word occurs. In addition to the usual autoencoder objective of
reconstruction, the representation at each node is also used to predict the class of the whole sentence
by applying a softmax neural network to the nodes.
The basic representations of words are randomly initialized vectors of dimensionality h, stored in a
matrix W where every row represents one word. This representation is enhanced using an embed-
ding matrix Lwhich is optimized during training. The ﬁnal representation of a word indexed bynis
obtained by Wn + Ln. This representation serves as the base representation for tree construction. 1
First, we assume the words to be the leaf nodes of the tree. Trees are then constructed by iteratively
joining two adjacent nodes using an autoencoder which consists of two layers. The encoding layer
takes two nodes n1 and n2 and outputs a combined representation r:
r= f(A1[n1; n2] + b1)
Subsequently, the reconstruction layer tries to reproduce the original inputs:
[n1,n2] = A2 r+ b2
f is a non-linear function. Ax is the weight matrix, bx the bias for the respective layer. Note that
the dimensionality of rneeds to be the same as of n1 and n2 so that the autoencoder can be applied
recursively.
The resulting output of the autoencoder (again of dimensionality h) serves as the representation of
a new node that has the two encoded nodes as its children. The combination operation is carried out
greedily by autoencoding the pair of nodes ﬁrst that has minimal reconstruction error Erec.
Each node output is then used to predict the sentence label individually using a softmax layer:
c= softmax(Al r),
where Al is a weight matrix and rthe representation of a node.
The representations have an inﬂuence on both reconstruction and classiﬁcation. Therefore, there are
two objectives to be minimized: the reconstruction error Erec that speciﬁes how well the resulting
node represents the two children, and a classiﬁcation error Ecl that measures how well the correct
label of the sentence can be predicted from the information at the node.Erec is the Euclidean distance
between the original and reconstructed nodes.Ecl is the cross-entropy error between the correct label
and the output of a softmax layer that is trained. In particular, the embedding matrix Lis optimized
by calculating the classiﬁcation errors over all words in the training set. For batch optimization
arithmetic means of the errors ¯Erec and ¯Ecl and the corresponding gradients are calculated over all
nodes. Higher-order nodes are penalized in favor of leaf nodes with a factor β. Erec and ¯Ecl are
added with weight α,1 −α. The model parameters are optimized with L-BFGS.
After the autoencoder is trained, a feature extraction step follows. Following Socher et al. (2011),
we performed this step differently from the way the autoencoder is optimized. First, we recursively
1Some of these details are best described in the RAE implementation available athttp://www.socher.
org.
2
apply the aforementioned greedy autoencoder to build a tree for each sentence. In contrast to the
RAE training, where the errors of individual nodes are averaged, we ﬁrst calculate the arithmetic
mean of all node features to get a single feature for the tree. Let f1 ... + fn be the features of
the nnodes in a tree. Feature extraction returns ¯f =
∑
i fi
n as the tree’s representation. Finally, a
softmax neural network is trained on this representation. Taking the mean of all nodes resembles
convolution operations which have been successfully applied in NLP previously, e.g. by Collobert
et al. (2011) who calculate the maximum over the dimensions of their representation vectors. The
original implementation (Socher et al., 2011) also uses the top feature separately. We leave this
feature out in our experiments as it did not improve the results signiﬁcantly.
The RAE has several parameters that need to be set. First, the weighting of reconstruction and
classiﬁcation error α. Second, the penalty of higher-order node errors versus leaf-level errors β. In
addition, all activation matrices vectors are regularized with the L2 norm. We adopt the parameter
settings used in (Socher et al., 2011).
3 Structural simpliﬁcations
In a complex model like the RAE it is difﬁcult to see which components are responsible for the
results it achieves. In order to analyze these structures, we try to simplify them automatically.
Evaluating the model on a task will then show us whether the structure omitted made a contribution.
In the following sections we will present three ways of structural simpliﬁcation which we will apply
to the RAE trees.
3.1 Tree level cuts
As a ﬁrst, simple approach to determine the inﬂuence of higher level nodes in the tree, we simply
remove nodes from the representation. One straightforward operation that achieves this is a level
cut.
We count levels starting at the leaves, the basic units for the RAE. All terminal nodes t are
deﬁned to have level l(t) = 1 , and each non-terminal n with children ⟨c1,c2⟩has the level
l(n) = max(l(c1),l(c2)). In practice, we compute the full tree and then prune away all nodes
that have a level higher than lmax. We call this a (tree) level cut.
3.2 Subtree selection
Another approach to simpliﬁcation follows from the idea that not every word is important for sen-
timent classiﬁcation, but rather that there is a region in the sentence that is sufﬁcient to recognize
sentiment (cf. (Tu et al., 2012)). A good example is the tree in Figure 1(b). In order to predict the
correct sentiment of the sentence, it is sufﬁcient to analyze the second clause (“it never took off and
always seemed static”). This way of simpliﬁcation is orthogonal to level cuts. Level cuts reduce
the amount of structure induced by the autoencoder but keep the complete input. Subtree selection
reduces the input, but it makes use of and is based on the full tree representation.
In order to select a region, we greedily select a central word: We apply the softmax function of the
autoencoder to each word in the sequence and pick the one with the lowest Ecl as the central word.
For training examples, we compute the error for the gold class. For testing examples, we compute
the score for all classes and select the word with the overall minimal error. Starting from this point,
we select the largest subtree of the tree produced by the RAE whose top node nhas a level of at
most lmax.
3.3 Window selection
Related to this is the window approach. Here, we again identify a central word as shown before
and take the representations of all words within a window wto either side as input to the classiﬁer,
where the central word is the left- or rightmost word, respectively. All other words are ignored. For
example w= 3 means that we take the two words to the left and to the right of the central word and
drop everything else; and w= 1 only uses the central word and no context. In this approach, no tree
structures are used, only the embeddings.
3
4 Task
We use the same task and data set for our RAE investigation as (Socher et al., 2011): sentiment
classiﬁcation for the sentence polarity dataset by Pang and Lee (2005). It contains 10,662 sentences
from movie reviews that were manually labeled as expressing positive or negative sentiment towards
the movie. We use the implementation provided at http://www.socher.org. We set the
hidden layer dimensionality to h= 50. All experiments are carried out on a random 90/10 training-
testing split. Accuracy is used as the evaluation measure since the class distribution in the dataset is
balanced.
5 Error Analysis
Error analysis proves to be difﬁcult for automatically generated representations. In general, the di-
mensions produced by autoencoding in NLP applications cannot be interpreted easily. Therefore,
we resort to empirical evaluation in the context of our task. In this section, we will provide analyses
conducted by human annotators of two properties of the trees: syntactic and semantic composition-
ality.
5.1 Syntactic Coherence
Naturally, it is tempting to think of RAE tree structures in terms of syntactic analysis. In this section,
we will show that there is a large divergence between traditional syntactic trees as most theories of
grammar would posit and the trees produced by RAE. We analyze two phenomena: coordinating
conjunctions and negation. While coordinations are notoriously difﬁcult even in supervised prob-
lems, negations are less problematic (Collins, 1999; McDonald, 2006). We asked 2 humans to judge
whether the parse of 10 randomly selected examples for each of the phenomena was correct with
respect to the phenomenon. None of the 20 parses (10 for each of the two phenomena) were unani-
mously determined to be correct by the human judges.
The example trees in Figure 1 illustrate these results. We will ﬁrst take a look at examples for
negation. In sentence 1(a), the autoencoder used not at a low level in the tree, constituting a mod-
iﬁcation of a. Their joint representation is itself joined with bad. The correct analysis would use
not as a modiﬁer to a joint structure a bad journeywhere bad and journey are combined directly.
Sentences 1(b) and 1(c) represent cases where the autoencoder introduced long distances between
the negating and the negated phrase (never to took offand not to describe).
We now turn to coordinations. In sentence 1(b), we ﬁnd an instance of a coordination of two clauses.
The clause always seemed staticshould receive a joint analysis and should then be modiﬁed byand.
Instead, and is put in a subtree containing two words from each of the coordinated clauses.
The underlying reason for the resulting structures may actually arise through the property of greed-
iness. As RAEs are trained greedily by joining the least error-prone combinations ﬁrst, pairings of
frequent words are common. For example, in sentence 1(a), frequent words are joined ﬁrst ( not to
a, and all to .). The most uncommon words are added last ( bad and journey). In around 75% of all
occurrences, periods are adjoined directly to their neighbors, which is not desirable from a syntactic
point of view.
5.2 Semantic Coherence
We will now analyze the behavior of RAEs from a semantic point of view. Sentiment analysis, our
example task, relies heavily on semantic composition.
Usually, sentiment is only carried by a small number of expressions, for example adjectives like
great or awful in combination with their close syntactic environment (Tu et al., 2012). The sentiment
of an expression can then be modiﬁed by applying an intensifying or reversing construction. A
simple check for whether RAEs are able to learn compositionality is to check the produced trees for
instances of these modiﬁers and see whether they behave as expected.
Intensiﬁers such as very or little are difﬁcult to analyze as there is no straightforwardly quantiﬁable
result that one would expect to occur, especially in the case of discrete labels as opposed to continu-
4
(a) Sentence 1: not a bad journey at all .
(b) Sentence 2: though everything might be literate and smart , it never took off and always seemed static .
(c) Sentence 3: the *UNKNOWN* elaborate continuation of “ the lord of the rings ” trilogy is so huge that a
column of words can not adequately describe *UNKNOWN* peter *UNKNOWN* ’s expanded vision of *UN-
KNOWN* . r . r . *UNKNOWN* ’s *UNKNOWN* .
Figure 1: Example trees. Leaf nodes contain the word that is encoded by them. All nodes contain
the sentiment predicted using softmax, 1 being positive and 0 negative sentiment. We follow (Socher
et al., 2011) and replace words for which no embedding is available by “*UNKNOWN*”.
5
lmax/w extract train+
extract
no-
embed subtree window
1 77.30 77.67 58.07 25.33 25.33
2 75.98 68.95 62.89 25.33 30.96
3 75.61 54.60 66.98 26.17 73.55
5 74.86 52.60 69.14 27.67 74.20
7 76.08 54.04 72.20 55.25 74.86
10 76.17 62.02 74.11 71.58 75.61
15 76.75 63.23 77.39 77.02 77.20
20 76.75 63.60 77.49 76.74 77.20
∞ 76.75 77.24 77.49 77.20 77.21
Table 1: Accuracy using different structure reduction techniques.
ous sentiment scores. Therefore, we only consider reversers in this analysis. There is no consensus
as to which words constitute the set of reversers. Often, reversing properties are context-dependent
(Polanyi and Zaenen, 2006). We therefore picked a small set of reversers with general applicability:
not/n’t, no, and never.
To check whether reversal occurs in a tree, we ﬁrst calculate the classiﬁcation decision by evaluating
the softmax decision function at each node. We search the trees for occurrences of any reverser and
check whether its sibling and its parent node are assigned opposite classes, which should be the
case if the reverser was correctly applied. We ﬁnd that reversal happens only in around 31% of all
reverser occurrences.
Of course, since reversing is context-dependent, we need human input to verify this result. From
all trees containing reversers, we randomly selected 3 trees in which the reverser reverses sentiment
and 7 trees in which the reverser does not reverse sentiment (the goal being to mimick the 31% rate
of reversals). We asked 2 human judges to check the examples for correct reversal. The judges
unanimously found that only 3 out of 10 candidates are behaving correctly, conﬁrming that reversal
is very likely not a property captured correctly by the RAE model.
We again turn to Figure 1 for examples of errors. In sentence 1(a), not does not reverse the polarity
of a – which is correct. However, modifying bad with the resulting structure still does not reverse.
The polarity of the whole sentence seems to be determined by the polarity of journey which gets
reversed at the top node, leading to misclassiﬁcation. In sentence 1(b), never should reverse took
off, yielding a negative sentiment overall. However at the point where the two phrases are joined,
the topmost node depicted, positive sentiment is predicted.
We reiterate that the effect of reversers is quite complex and in many contexts – e.g., “not awesome,
but pretty good” – they do not simply reverse sentiment. However, the examples seem to show that
the syntactic and semantic role of reversers is not modeled well in thoses cases where they act as
simple reversers.
6 Automatic structural simpliﬁcation
In the previous section we showed that the structures produced by RAEs cannot be easily inter-
preted in terms of traditional linguistic categories from syntax and semantics. We will now turn to
empirically evaluating the contributions of these structures in a practical classiﬁcation task.
6.1 Level cuts during feature extraction
In the ﬁrst experiment, we train the autoencoders to produce full trees and only apply level cuts in
the feature extraction. We report accuracies in Table 1, column extract for different values of lmax.
The trees produced by the RAE on the whole dataset have a mean height of 10 and a maximum
height of 23.
6
First note that the best accuracy is achieved by cutting directly above the leaves, i.e. using no trees
at all. Increasing the maximum level lowers accuracy signiﬁcantly; e.g. using lmax = 5 , we lose
over 2 percentage points compared to lmax = 0 . Only when lmax is increased further, accuracy
recovers. This result suggests that the leaf representations – the embeddings – carry great weight in
classiﬁcation.
In order to demonstrate the signiﬁcance of the embeddings, we again train full RAE trees but resort
to the random representations during feature extraction. The results of this run are shown in Table 1,
column noembed.
Naturally, using random representations only we achieve low accuracy. Note that the results are
still over chance level, an effect which may be caused by the random representations being sim-
ilar to low-dimensional random indexing (Kanerva et al., 2000). Nevertheless, using higher-level
tree representations successively increases accuracy to a similar level as observed in the previous
experiment.
Our interpretation of this experiment is that while the trees seem to be able to create useful represen-
tations of the underlying words, these representations are redundant with respect to the embeddings.
Combining both does not lead to improved classiﬁcation accuracy.
6.2 Level cuts during training
One could argue that using a fully-trained RAE to extract pruned trees is unfair since the model is
still able to use the full information induced during training. Taking the level cut approach one step
further, we also cut the trees during training, using the same maximum level. Column train+extract
in Table 1 shows the results for this experiment.
First, we observe that we get a well-performing model if the maximum level is 1 in both RAE
training and feature extraction. This is not surprising as we are not using the node-combining part
of the RAE at all which makes this particular model equal to the one with maximum level 1 in the
previous experiment. Next, we can see that accuracy drops quickly as we introduce more levels
and only recovers after raising the threshold to ∞, using full trees. A possible explanation for this
phenomenon is that when enforcing low levels there are also fewer training instances for the RAE
and thus the resulting models are worse. Another possibility is that when full trees are constructed,
all applications of the RAE depend on each other since errors are propagated through the structure.
Thus, inconsistencies should be optimized away. However, there are fewer inconsistencies in lower-
level cuts since the resulting subtrees are likely to be disconnected.
These experiments show that the best accuracy is achieved by a model that does not use the tree
structures at all. Our conclusion from this evidence is that the strength of the RAE lies in the
embeddings, not in the induced tree structure.
6.3 Subtree selection
We now turn to subtree selection. As stated previously, sentiment is a local phenomenon, so it might
be sufﬁcient to use part of a sentence to classify the data. Table 1, column sub shows the results for
this experiment.
We observe low accuracies for low lmax. Only when using large contexts (recall that the maximum
number of levels is around 23), the results become competitive. From this experiment, it is not
clear whether the height of the trees or the size of the contexts – which grows with the height – is
responsible for the gain. We will investigate this issue in the following section.
6.4 Window selection
As a last experiment, we concentrate on the embeddings as they seem to be sufﬁcient to achieve high
accuracies on this task. This will also show whether subtrees or embedded words were responsible
for the improvements with increasing tree height in the previous section.
We vary the window sizewstarting from 0 which is only the word itself, to∞which is the maximum
number of words. The data has a mean sentence length of around 21 words and a maximum sentence
length of 63 words. Results are shown in Table 1, column win.
7
While a small window size (0 or 1) produces bad results – which might be an effect of choosing
the wrong most conﬁdent word resulting in strong overﬁtting – vast improvements are visible soon.
Taking a window of 15 words (in each direction) is sufﬁcient. There are around 16% of the data that
have more than 31 words, so it seems that for sentences of that length, there are no context effects
that the model can exploit.
6.5 Discussion
We presented multiple experiments in which we simpliﬁed the RAE tree structures. All of these
experiments point towards the embedding having the most inﬂuence on the end result. If embeddings
are not used, accuracy drops almost to chance level. Using full trees and no embeddings seem to
have the same effect as using only embeddings. However, using both representations together does
not yield any improvement. This suggests that there is a large overlap between what the trees model
and what the embeddings model.
7 Conclusion
In this paper, we conducted two different experiments concerning the structures learned and gener-
ated by Semi-Supervised Recursive Autoencoders.
First, we automatically reduced the structure in different ways and showed that on our sentiment
analysis task the embedded words were sufﬁcient to achieve state-of-the-art accuracy. Our experi-
ments on window selection suggest that a structure as simple as a well-chosen subset of the words
in a sentence produces a good model.
In a human evaluation, we showed that there is no simple way to interpret the structures produced
by RAEs in terms of traditional linguistic categories of syntax and semantics.
Overall, we conclude that structural simpliﬁcations are possible at least for a sentiment analysis task.
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft through the Sonderforschungs-
bereich 732.
References
Collins, M., 1999. Head-driven statistical models for natural language parsing. Ph.D. thesis, Univer-
sity of Pennsylvania.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P., Nov. 2011. Natural
language processing (almost) from scratch. J. Mach. Learn. Res. 999888, 2493–2537.
Deselaers, T., Hasan, S., Bender, O., Ney, H., 2009. A deep learning approach to machine translit-
eration. In: Proceedings of the Fourth Workshop on Statistical Machine Translation. StatMT ’09.
Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 233–241.
Glorot, X., Bordes, A., Bengio, Y ., June 2011. Domain adaptation for large-scale sentiment classi-
ﬁcation: A deep learning approach. In: Getoor, L., Scheffer, T. (Eds.), Proceedings of the 28th
International Conference on Machine Learning (ICML-11). ICML ’11. ACM, New York, NY ,
USA, pp. 513–520.
Kanerva, P., Kristoferson, J., Holst, A., 2000. Random indexing of text samples for latent semantic
analysis. In: In Proceedings of the 22nd Annual Conference of the Cognitive Science Society.
Erlbaum, pp. 103–6.
McDonald, R., 2006. Discriminative learning and spanning tree algorithms for dependency parsing.
Ph.D. thesis, University of Pennsylvania.
Pang, B., Lee, L., June 2005. Seeing stars: Exploiting class relationships for sentiment categoriza-
tion with respect to rating scales. In: Proceedings of the 43rd Annual Meeting of the Association
for Computational Linguistics (ACL’05). Association for Computational Linguistics, Ann Arbor,
Michigan, pp. 115–124.
8
Polanyi, L., Zaenen, A., 2006. Contextual valence shifters. Computing attitude and affect in text:
Theory and applications, 1–10.
Socher, R., Manning, C., Ng, A., 2010. Learning continuous phrase representations and syntactic
parsing with recursive neural networks. In: Proceedings of the NIPS-2010 Deep Learning and
Unsupervised Feature Learning Workshop.
Socher, R., Pennington, J., Huang, E. H., Ng, A. Y ., Manning, C. D., 2011. Semi-Supervised Re-
cursive Autoencoders for Predicting Sentiment Distributions. In: Proceedings of the 2011 Con-
ference on Empirical Methods in Natural Language Processing (EMNLP).
Tu, Z., He, Y ., Foster, J., van Genabith, J., Liu, Q., Lin, S., 2012. Identifying high-impact sub-
structures for convolution kernels in document-level sentiment classiﬁcation. In: Proceedings of
the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - V olume
2. ACL ’12. Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 338–343.
9


Peer review:
[Review]:
This paper analyzes recursive autoencoders for a binary sentiment analysis task. 
The authors include two types of analyses: looking at example trees for syntactic and semantic structure and analyzing performance when the induced tree structures are cut at various levels.
More in depth analysis of these new models is definitely an interesting task. Unfortunately, the presented analyses and conclusions are incomplete or flawed.

Tree Cutting Analysis:
This experiment explores the interesting question of how important the word vectors and tree structures are for the RAE.
The authors incorrectly conclude that 'the strength of the RAE lies in the embeddings, not in the induced tree structure'.
This conclusion is reached by comparing the following two models (among others):
1) A word vector average model with no tree structures that uses about 50x10,000 parameters (50 dimensional word vectors and a vocabulary of around 10,000 words) and reaches 77.67% accuracy.
2) A RAE model with random word vectors that uses 50 x 100 parameters and gets 77.49% accuracy.

An accuracy difference of 0.18% on a test set of ~1000 trees means that ~2 sentences are classified differently and is not statistically significant. So the results of both models are the same.
That means that the RAE trees achieved the same performance with 1/100 of the parameters of the word vectors. So, the tree structures seem to work pretty well, even on top of random word vectors.

A good comparison would be between models with the same number of parameters. Both models could easily be increased or decreased in size.

One possible take away message could have been that the benefits of RAE tree structures and word embeddings are equal but performance does not increase when both are combined in a task that only has a single label for a full sentence.
But even that one is difficult:
All columns of the main results table (cutting trees) have the same top performance when it comes to statistical significance, so it would have also been good to look at another dataset.
Another problem is that the RAE induced vectors are only used by averaging all vectors in the tree.  

More important analyses into the model could explore what the higher node vectors are capturing by themselves instead of only in an average with all lower vectors.


Tree Structure Analysis:
The first analysis is about the induced tree structures and finds that that they do not follow traditional parsing trees. 
This was already pointed out in the original RAE paper and they show several examples of phrases that are cut off.
An interesting comparison here would have been to apply the algorithm on correct parse trees and analyze if it makes a difference in performance.


The second analysis is about sentiment reversal, such as 'not bad'. 
Unfortunately, the given binary examples are hard to interpret. 
Are phrases like 'not bad' positive in the original training data? It's not clear to me that 'not bad' is a very positive phrase.
Do the probabilities change in the right direction? When does it work and when does it not work? Is the sentiment of the negated phrase wrong or is the negation pushing in the wrong direction?
In order to understand what the model should learn, it would have been interesting to see if the effects are even in the training dataset.
Another interesting analysis would be to construct some simple examples where the reversal is much clearer like 'really not good'.


The paper is well written.
Only one typo: E_cE and E_eC both used.