Alleviating “Posterior Collapse” in Deep Topic Models via Policy Gradient
Anonymous Author(s)
AffiliationAddress email
Abstract
Deep topic models have been proven as a promising way to extract hierarchical latent representations from documents represented as high-dimensional bag-of-wordsvectors.
However, the representation capability of existing deep topic models isstill limited by the phenomenon of “ posterior collapse ”, which has been widelycriticized in deep generative models, resulting in the higher-level latent representations exhibiting similar or meaningless patterns.
To this end, in this paper, we firstdevelop a novel deep-coupling generative process for existing deep topic models,which incorporates skip connections into the generation of documents, enforcingstrong links between the document and its multi-layer latent representations.
Afterthat, utilizing data augmentation techniques, we reformulate the deep-couplinggenerative process as a Markov decision process and develop a corresponding
Policy Gradient (PG)
based training algorithm, which can further alleviate theinformation reduction at higher layers.
Extensive experiments demonstrate thatour developed methods can effectively alleviate “ posterior collapse ” in deep topicmodels, contributing to providing higher-quality latent document representations.
Introduction
Topic modeling has become a successful technique for text analysis and been widely applied tovarious problems in machine learning (ML)
[1, 2] and natural language processing (NLP)
[3, 4] overthe past two decades.
Representing documents as bag-of-words (BoW) vectors, vanilla probabilistictopic models (PTMs), with latent Dirichlet allocation (LDA)
[5] being the best known representative,typically formulate each document as a mixture over latent topics where each topic is characterizedby a distribution over the terms of the vocabulary and describes an interpretable semantic concept.
Although being widely used, the modeling capability of these shallow topic models is still restrictedby their single-layer structure and has difficulty in exploring hierarchical thematic structures.
Tothis end, a series of deep topic models [6, 7, 8] have been developed to extract multi-layer documentrepresentations from a text corpus, providing a more intuitive way for users to understand text data.
Recently, benefiting from the development of deep neural networks (DNNs), there has been an emerging research interest to develop neural topic models (NTMs) to boost the performance, efficiency,and usability of topic modeling with DNNs.
Specifically, following the framework of variationalautoencoder (VAE) [9], most NTMs [10, 11, 12] construct a variational inference network (encoder)to project each document into its stochastic latent representation, and then reconstruct the corresponding BoW observation with a stochastic/deterministic decoder.
By modeling the inference/generativeprocess with DNNs, these NTMs are more flexible and scalable than traditional Bayesian PTMs,contributing to performing large-scale downstream tasks, especially in NLP tasks [13, 14].
“ Posterior collapse ” has been widely criticized in the field of generative model [15, 16], and theoccurrence of this phenomenon will cause the approximated posterior q ϕ ( z | x ) collapses it noninformation prior distribution p θ ( z ) , leading their KL divergence to be colse to zero [9, 15, 16, 17].
For deep topic models, despite achieving attractive performance, existing PTMs or NTMs still sufferfrom different degrees of “ posterior collapse ”, which causes their exhibiting similar or meaninglesspatterns at higher layers [18, 19, 20].
Although there have been several deep NTMs [19, 20] tryingto alleviate this issue by constructing more flexible inference networks, the collapse phenomenonin deep NTMs may not be solved in essence, because the true posterior provided by the generativemodel and the objective function for optimization remain almost unchanged [17].
To extract higher-quality hierarchical latent document representations, in this paper, we develop adeep-coupling generative process equipped with a Policy Gradients (PG) based training algorithm forexisting deep topic models.
The main contributions of this work are as follows:
• We develop a deep-coupling generative process for deep topic models, which incorporatesskip connections into the generation of documents to alleviate “ posterior collapse ”.•
We take a specific NTM as an example to explain how to construct a deep topic model withthe deep coupling generation process, and develop a deep-coupling hierarchical EmbeddingTopic Model ( dc -ETM), which can be extended to other deep topic models.•
Utilizing the property of sequence-like generation process, we design a PG-based trainingalgorithm for dc -ETM, which can further alleviate the information reduction at higher layers.
• Compared to existing deep topic models, extensive experimental results show that dc -ETMscan lead to less “ posterior collapse ” and provide higher-quality latent representations.
Related Work
Probabilistic Topic Model: Deep PTMs [6, 7, 8, 21, 22] are developed to infer multi-layer documentrepresentations, whose adjacent layers are connected with specific factorization.
For instance, gammabelief network (GBN) [7] is constructed via factorizing the shape parameters of the gamma distributedlatent representations; DPFA [6] extends PFA [23] into a multi-layer version but is restricted to modelbinary topic usage patterns; DirBN [8] is developed via factorizing the Dirichlet distributed topicmatrix.
Although providing readily interpretable multi-layer latent document representations, therepresentation capability of these deep PTMs is limited by adopting CRT distribution to upwardpropagate data information to higher layers with their backbones [18].
Neural Topic Model: Most existing NTMs [11, 19, 24, 20] can be viewed as extensions of PTMsunder the VAE framework and focus on modeling the generative/inference process with DNNs.
Forinstance, one popular research direction of NTMs is to develop more flexible inference network withreparametrization tricks [11, 19] and the other could be incorporating word embeddings into thegenerative model [24, 20].
However, as far as we know, few efforts have been made to alleviate thephenomenon of “ posterior collapse ” in NTMs by modifying its generative process, which is a greatchallenge under the framework of topic modeling and also the main contribution of this work.
Besides, distinct from the way of combining reinforcement learning (RL) with topic models inprevious works [25, 26, 27], our work is the first to formulate the topic modeling generative processas a sequential decision making one to incorporate RL-based training algorithms, which focuses onproviding higher-quality latent document representations by alleviating “posterior collapse”.
Deep-Coupling Generative Process for Deep Topic Models
To give an intuitive insight on “ posterior collapse ” in deep topic models, we provide a detailedintroduction for “ posterior collapse ” in Appendix and then visualize the higher-level topics learnedby a recent popular NTM named SawETM [20] in Fig. 3, which exhibit similar semantic patterns andlimit its representation capability.
Then, we take SawETM as an example, but not limited to this, toillustrate how to construct a deep topic model with the deep coupling generation process, leadingto a novel dc -ETM in Fig. 1(c).
Compared to the usual structures of deep PTMs and NTMs shownin Fig. 1(a) and 1(b), besides the design of inference network, the main difference of dc -ETM isincorporating skip connections into the generation of documents, enforcing strong links between thedocument and its multi-layer latent representations to alleviate “ posterior collapse ”.
3.1 Deep-Coupling Hierarchical Embedding Topic Model
As a usual VAE-like model, the developed dc -ETM consists of a generative model (decoder) and aninference network (encoder).
Below, we focus on presenting the generative model of dc -ETM, which
(1) n  (2) n  (3) n  × (0) (0) T K D    (3) (3) D K    (3) ˆ  × (0) (0) T K D    (2) (2) D K    (2) ˆ  × (0) (0) T K D    (1) (1) D K    (1) ˆ  × (1) (1) T K D    (2) (2) D K    (2)  × (3) (3) D K    (2) (2) T K D    (1) n x (2) n x (3) n x n x (1) n h (2) n h (3) n h n x
[Table]
n h (3)  n x (1) n h (2) n h (3) n h (3) n  (2) n  (1) n  (3)  (2)  (3) n  (2) n  (1) n  n x (3)  (2)  (1)  (3) n  (2) n  (1) n  n x (3)  (2)  (1)  (3) n  (2) n  (1) n  (3)  (2)  n x (1) n h (2) n h (3) n h (3) n  (2) n  (1) n  (3)  (2)
 n x (3) n x (2) n x (1) n x
[Table]
Inference
[Table]
Inference
Generative (1)  (2)  (3)  (a) Deep PTM (1) n  (2) n  (3) n  × (0) (0) T K D    (3) (3) D K    (3) ˆ  × (0) (0) T K D    (2) (2) D K    (2) ˆ  × (0) (0) T K D    (1) (1) D K    (1) ˆ  × (1) (1) T K D    (2) (2) D K    (2)  × (3) (3) D K    (2) (2) T K D    (1) n x (2) n x (3) n x n x (1) n h (2) n h (3) n h n x
[Table]
n h (3)  n x (1) n h (2) n h (3) n h (3) n  (2) n  (1) n  (3)  (2)  (3) n  (2) n  (1) n  n x (3)  (2)  (1)  (3) n  (2) n  (1) n  n x (3)  (2)  (1)  (3) n  (2) n  (1) n  (3)  (2)  n x (1) n h (2) n h (3) n h (3) n  (2) n  (1) n  (3)  (2)
 n x (3) n x (2) n x (1) n x
[Table]
Inference
Generative
Inference
Generative (1)  (2)  (3)  (b
(1) n  (2) n  (3) n  × (0) (0) T K D    (3) (3) D K    (3) ˆ  × (0) (0) T K D    (2) (2) D K    (2) ˆ  × (0) (0) T K D    (1) (1) D K    (1) ˆ  × (1) (1) T K D    (2) (2) D K    (2)  × (3) (3) D K    (2) (2) T K D    (1) n x (2)
[Figure]
x (
[Figure]
x
[Figure]
x (
[Figure]
PTM, (b) deep NTM, and (c) dc -ETM developed in this paper, where the symbol definitions are consistent with those in Sec. 3.1.
can be flexibly applied for other deep topic models to alleviate “ posterior collapse ”, and leave thedetails of the inference network to Appendix A.
Generative Model:
Given a text corpus consisting of N documents X = { x n } Nn =1 , each documentcan be represented as a high-dimensional sparse BoW vector x n ∈ Z K (0) , where
Z = { 0 , 1 , ... } and
K (0) denotes the vocabulary size.
Then, from top to bottom, the generative model of the dc -ETMwith L hidden layers can be formulated as
[Equation]
x n ∼ Pois (
L (cid:88) l = α ( l ) ˆ Φ ( l ) θ ( l ) n ) , α = Softmax ( ξ ) , ϕ ( l ) k
= Softmax ( β ( l
[Equation]
1) T β (
[Equation]
) k ) , l = 1 , ..., L − 1 , where, Φ ( l ) ∈ R K ( l − 1) × K ( l ) + denotes the topic matrix (factor loading) and each column ϕ ( l ) k ∈
R K ( l − 1) + indicates a specific topic (factor) at layer l ; θ ( l ) n ∈ R K ( l ) + denotes the gamma distributedlatent representation (topic proportions) at layer l , K ( l ) denotes the number of hidden units (topics)at layer l .
Under the Poisson likelihood, the observed multivariate count vector x n is first factorizedinto L equal-size latent matrix { α ( l ) ˆ Φ ( l ) θ ( l ) n } Ll =1 , where, ˆ Φ ( l ) ∈ R K (0)
× K ( l ) + can be regarded as theprojection of topic matrix Φ ( l ) to the observation space and the detailed definition will be discussedin the next paragraph; α ( l ) denotes the importance weight of ˆ Φ ( l ) θ ( l ) n for generating the observationx n , and the summation of the whole weight vector α ∈ R L + is constrained to be equal to one with a
Softmax normalization.
Then, the latent representation θ ( l ) n at layer l is further factorized into theproduct of the topic matrix Φ ( l +1) ∈ R K
( l ) × K ( l +1) + and topic proportions θ ( l +1) n ∈ R K ( l +1)
+ at the nextlayer under the shape of gamma distribution.
The top layer’s latent representation θ ( L ) n shares the samegamma shape parameters r ∈ R K ( L )
+
and we apply a gamma distributed prior on the scale parametersc ( l ) n for l ∈ { 2 , ..., L + 1 } .
With the recent popular distributed topic representation in NTMs [24, 28],each topic ϕ ( l ) k is treated as the result of applying a Softmax normalization on the inner product ofits distributed representation β ( l ) k ∈ R D and topic embedding matrix β ( l − 1) ∈
R D × K ( l − 1) at theprevious layer, where D denotes the dimension of the embedding space.
The projections of topic matrices to the observation space, denoted as { ˆ Φ ( l ) } Ll =1 , build the straightforward connections between the document x n and its multi-layer latent representations { θ ( l ) n } Ll =1 ,which alleviates the information reduction at higher layers by sharing the pressure of documentmodeling with all hidden layers.
To reduce the computation and storage cost of the developeddc -ETM, we develop two variants for ˆ ϕ ( l ) k ∈ R K (0) + without introducing any extra parameter.
Theone variant is adopting the property of topic hierarchy elaborated in Sec.
3.2 to obtain each ˆ ϕ ( l ) k bysuccessively multiplying topic matrices at lower layers as
[Equation]
and the other variant is treating the projection ˆ ϕ ( l ) k as the result of the inner product of its distributedrepresentation β ( l ) k
and the word embedding matrix β (0) at the observed space, as follows
[Equation]
We emphasize that the first variant can be used to extend most existing deep topic models, while thelatter is limited to NTMs equipped with topic embedding techniques.
We use the suffix − α and − βto distinguish the variants defined in Eq.
(2) and Eq.
(3), and their detailed implementations can befound in Appendix I.
Generally speaking, the deep-coupling generative process in dc -ETM not only preserves the hierarchyof traditional deep topic models, leading to multi-layer document representations to enhance themodeling capability and interpretability, but also alleviates the issue that the amount of informationwill decrease rapidly with the network going deeper, benefiting from building the straightforwardconnections between observation x n and its higher-level latent representations { θ ( l ) n } l> 1 .
Besidesalleviating “ posterior collapse ”, the characteristics of deep-coupling network structure of dc -ETMalso brings us a new view to design the corresponding inference network and training algorithm.
Inference Network : The details of the inference network of dc -ETM can be found in Appendix A.
3.2 Model Property
Sequence-like Generative Process: Taking advantages of the properties of the Poisson distribution,the original generative process of the observed data x n defined in Eq.
(1) can be rewritten as:
[Equation]
where x ( l ) n denotes the augmented observation at layer l , and is generated from the Poisson distributionwith a rate of α ( l ) ˆ Φ ( l ) θ ( l ) n .
Then, the observed data x n can be regarded as not only the summationover these augmented vectors { x ( l ) n } Ll =1 , but also equal to the weighted summation over the latentvectors { ˆ Φ ( l ) θ ( l ) n } Ll =1 on the mean, where the weight vector α satisfies the constraint (cid:80)
Ll =
1 α ( l ) = 1 .
Rethinking the generative process of the developed dc -ETM reformulated in Eq.
(4), the set ofaugmented observation vectors { x ( l ) n } Ll =1 can naturally form an observation sequence [ x ( L ) n , ..., x (1) n ]by sorting these vectors according to their dependencies in the generative process (from deep toshallow).
For each hidden layer (time step) l , the generative process will first incorporate the priorinformation passing from deeper hidden layers { θ ( t ) n } t>l , and then generate the latent representationθ ( l ) n at the current layer (time step), which not only is supposed to generate the current observationvector x ( l ) n under the Poisson likelihood, but also introduces the information into the shape parameterof the following gamma distributed latent representation θ ( l − 1) n at the next layer (time step).
Thus, the deep-coupling generative process of dc -ETM originally defined in Eq.
(1) can be naturallyreinterpreted from the perspective of sequence generation, and its reformulation defined in Eq.
(4)can be also equivalently reformulated as:
[Equation]
providing an intuitive insight for the decomposition of the likelihood function in Sec. 4.1.
Hierarchical Semantic Topics:
The developed dc -ETM can naturally interpret each semantic topic ϕ ( l ) k at layer l by visualizing its projection to the vocabulary space calculated as{ [ (cid:81) l − 1 t =
Φ ( t ) ]
ϕ ( l ) k } K ( l ) k
=1 , and each document can also be roughly seen as a random mixture over
K ( l ) topics with θ ( l ) n being the corresponding topic proportions at layer l as
[Equation]
which can be obtained with the law of total expectation.
Moreover, similar to the underlying idea ofthe deep learning, the topics learned by dc -ETM tend to be more specific at lower (bottom) layersand those at higher (top) layers are more general, as shown in Fig.
Secondly, in dc -ETM, both words β (0) ∈ R D
× K (0) and hierarchical topics { β ( l ) ∈ R D
× K ( l ) } Ll =1are represented as embedding vectors under the same semantic space, contributing to intuitivelymeasuring and visualizing the distance between different topics (words), which has been proven to beeffective in capturing the underlying semantic structure as shown in Fig.
5(a).
Policy Gradient based Training Algorithm
4.1 ELBO of dc -ETM
As a VAE-like NTM, the developed dc -ETM can be trained like usual VAEs by directly maximizingthe evidence lower bound (ELBO), specifically as
[Equation]
where the first term is the expected log-likelihood and the other term is the Kullback–Leibler (KL)divergence from the prior p ( θ n ) to the variational posterior q ( θ n | x n ) .
Through introducing the augmented vectors { x ( l ) n } Ll =1 , the log-likelihood of x n in dc -ETM can beequivalently reformulated as
[Equation]
where the function in the second expectation term can be treated as the summation of the set oflog-likelihood of { x ( l ) n } Ll =1 .
Due to the hierarchical network structure, the KL divergence term canbe factorized as
[Equation]
where q ( θ ( l ) n |− ) is constructed by a Weibull-based inference network described in Appendix.
A andp ( θ ( l ) n | Φ ( l +1) , θ n ( l +1) ) satisfies a gamma prior in Eq.
(1), and their KL divergence has an analyticexpression, benefiting from adopting the Weibull reparameterization technique [19].
Combining the aforementioned derivations, the ELBO of dc -ETM can be equivalently rewritten as
[Equation]
[Equation]
which can be directly optimized with gradient-based methods to update both the encoder parametersΩ and decoder parameters Ψ in dc -ETM.
We emphasize that, after deriving the augmented vectors{ x ( l ) n } Ll =1 from x n via data augmentation technique [7], the first expectation term in L ( x n ) will be aconstant and the ELBO can be directly optimized by maximizing the following loss function
[Equation]
which can be roughly treated as the ELBO of a sequence [ x ( L ) n , ..., x (1) n ] generated from a sequenceof latent representations [ θ ( L ) n , ..., θ (1) n ] [29, 30], and naturally meets the sequence-like generativeprocess of dc -ETM as discussed in Sec. 3.2.
4.2 Optimization with Policy Gradient
Similar to RNN-based model, after augmenting { x ( l ) n } Ll =1 from x n , the loss function of dc -ETMdefined in Eq.
(11) is equal to the summation of L sub-loss functions, where each sub-loss functionˆ L ( l ) ( x ( l ) n ) can be equivalently regarded as a separate loss of a subsequence generation model that isonly a part of the whole sequential generative model and expected to output x ( l ) n at the final time stepl .
Inspired by the great success achieved by RL methods [31, 32, 33, 34] in learning a stable longsequence (Markov decision process) with high quality, we consider the sequence-like generationprocedure of a L -layer dc
-ETM
as a Markov decision process with L time steps, and develop a noveltraining mechanism based on Policy Gradient [33] for dc -ETM, which injects the future rewardsobtained from generating the suffix subsequence into each current sub-loss function ˆ L ( l ) ( x ( l ) n ) .
Specifically, we treat the whole dc -ETM as a stochastic policy network π ( a ( l ) n | s ( l ) n ) expected togenerate a fixed-length action sequence [ a ( L ) n , ..., a (1) n ] from the observation x n , defining the state s ( l ) nas { x n , { Φ ( t ) , θ ( t ) n } t>l } and the action a ( l ) n as α ( l ) ˆ Φ ( l ) θ ( l ) n .
For each time step l , given the currentstate s ( l ) n , the policy network π ( a ( l ) n | s ( l ) n ) will first sample θ ( l ) n from the inference network viaθ
[Equation]
n
[Equation]
12) and further obtain the corresponding action as
[Equation]
) which can be regarded as directly drawing from π ( a ( l ) n | s ( l ) n ) .
The state transition is deterministicafter an action has been chosen, indicating that the next state s ( l − 1) n = { x n , { Φ ( t ) , θ ( t ) n } t>l − 1 } if thecurrent state s ( l ) n = { x n , { Φ ( t ) , θ ( t ) n } t>l } and the action a ( l ) n = α ( l ) ˆ Φ ( l ) θ ( l ) n .
Then we take the separate loss ˆ L ( l ) ( x ( l ) n ) defined in Eq.
(11) as the immediate reward at the the timestep l , formulated as
[Equation]
and the action-value function can be formulated as
[Equation]
which indicates the expected accumulative reward starting from state s ( l ) n , taking action a ( l ) n , andthen generating the suffix subsequence [ a ( l − 1) n , ..., a (1) n ] with the policy network π ( a ( l ) n | s ( l ) n ) and thediscount factor 0 < γ ≤ 1 .
Following [31], the objective function of training dc -ETM with policy gradient can be estimated (onone episode) as
[Equation] [Equation]
where Ω and Ψ indicate the encoder and decoder parameters in dc -ETM respectively.
We note that theexpectation E [ · ] can be approximated by sampling methods based on the Weibull reparameterization,and the objective function can be directly optimized by advanced gradient descent algorithms, like
Adam [35] and RMSprop [36].
We provide the details of PG-based training algorithm in Appendix C.
[Figure]
Experiments
To evaluate the effectiveness of the developed dc -ETM and the corresponding policy gradients (PG)based training algorithm, we make extensive experiments on both quantitative and qualitative aspects.
Considering there are two dc -ETM variants as described in 3.1, we use the suffix − α and − β todistinguish the variants defined in Eq.
(2) and Eq.
(3) respectively, and highlight whether the dc -ETMis trained with the PG based algorithm in Sec.
5.1 Datasets and Baselines
Datasets: Four widely used document benchmarks, specifically R8 [37], 20Newsgroups ( 20News )[38], Reuters Corpus Volume I ( RCV1 ) [39] and World Wide Web Knowledge Base ( WebKB )
[40]are included in the following experiments.
We summarize the statistics of benchmarks in Appendix Dand follow the procedure in [20] to preprocess these documents to obtain their BoW representations.
Baselines: We compare the developed dc -ETMs with a series of topic models, which can be roughlydivided into two categories: 1) shallow topic models such as LDA [5], AVITM [11] and ETM [24],where LDA is a PTM and the others are NTMs; 2) deep topic models including PGBN [7], WHAI[19] and SawETM [20], where PGBN is a deep PTM and the others are deep NTMs.
We emphasizethat WHAI and SawETM are the most relevant strong baselines for comparison, both of whichprovide hierarchical Weibull-based latent document representations, and SawETM has achievedstate-of-the-art performance on unsupervised document modeling and clustering tasks.
Experimental Settings: To make a fair comparison, we set the same network structure for alldeep topic models as [256 , 128 , 64 , 32 , 16] from shallow to deep.
For PTMs, we use the defaulthyperparameter settings in their published papers and accelerate the Gibbs sampling with GPU.
For NTMs, we set the size of their hidden layers as 256, the embedding size as 100 for themincorporating word embeddings, like ETM, SawETM and dc -ETMs, and the mini-batch size as200.
For optimization, we adopt the same Adam optimizer [41] with a learning rate of 1e-2.
Allexperiments are performed with an Nvidia RTX 3090 GPU and implemented with PyTorch [42].
5.2 Quantitative Comparisons
To investigate whether the proposed skip-connection structures in both dc -ETM variants can alleviate“ posterior collapse ”, especially at higher layers, we compare them with other popular deep topicmodels in the first part.
Then, to investigate whether the mitigation of “ posterior collapse ” canimprove the quality of latent document representations at higher layers, we conduct more quantitativecomparisons in the rest parts.
We report the error bars in Appendix E.
Document Modeling:
In Fig. 2, for each deep topic model, we plot the curve of point log-likelihoodln p ( x n |− ) as a function of iterative epochs conditioned on the t -th-layer reconstruction ˆ Φ ( t ) θ ( t ) n ,which can be used to measure the degree of “ posterior collapse ” by the relevance between the datasample x n and its latent representation θ ( t ) n .
From the results, we can see that although SawETMand WHAI achieve a comparable performance with dc -ETMs on the first hidden layer in Fig.
2(a),their reconstruction quality decreases dramatically with the network going deeper in Fig. 2(b) and2(c), potentially reflecting that little data information can be propagated to higher layers of these traditional deep topic models.
Benefiting from introducing skip connections into the generativeprocess, dc -ETMs can significantly alleviate “ posterior collapse ” at higher hidden layers.
[Table]
Perplexity & Topic Diversity:
To make a more comprehensivequantitative comparison, we usethe average of heldout-word perplexities (the lower is the better)and topic diversities (the higher isthe better) across all hidden layers to measure the document modeling performance and topic quality of these deep topic modelswith { θ ( t ) n } Tt =1 and { Φ ( t ) n } Tt =1 ,respectively.
The experimentalsettings are consistent with thosein [20], and the experimental results have been exhibited in Table 3.
Benefiting from hierarchical network structures, the modeling capability of deep topic modelsgenerally outperform those shallow ones.
Thanks to enhancing the connections between the observation and multiple hidden layers with the deep-coupling generative process, the developed dc -ETMsachieve lower perplexity scores and provide higher-quality topics than traditional topic models.
Then,the PG-based training algorithm brings further performance improvement to our dc -ETMs.
[Table]
Document Clustering: To evaluate the quality of the extracted latent document representations ondownstream tasks, we considerdocument clustering, where weuse the topic models after training to extract the latent representations of the testing documentsand then use k-means to predict the clustering labels.
Usingthe Purity and Normalized Mutual Information (NMI) as metrics (the higher the better), theresults shown in Table 4 demonstrate that concatenating hierarchical latent document representations extracted by traditional deeptopic models cannot improve andeven hurt the clustering performance, potentially indicating thatthe latent representations at higher layers are meaningless.
However, distinct from traditional deeptopic models, the concatenation operation on the latent representations of dc -ETMs can significantlyimprove the performance, which can be attributed to enforcing strong links between the multi-layerrepresentations and the observation with the skip connections in the generation.
5.3 Qualitative Analysis
As discussed in Sec. 3.2, the developed dc -ETM inherits both the characteristics of hierarchical topicstructure and semantic topic embeddings.
Then we compare the hierarchical topics of a 5-layerdc -ETM trained on 20News with those learned by SawETM for qualitative analysis.
Topic Visualization: With the visualization techniques [7], we exhibit the 5 th-layer topics learnedby dc -ETM and SawETM on 20News in Fig.
3 and Fig.
7, where each topic is interpreted by itstop-10 words by sorting the word probabilities by descending order.
Obviously, the topics learned by
SawETM are quite similar, explaining the reason why concatenating its hierarchical latent documentrepresentations cannot improve and even hurt the performance on downstream tasks.
On the contrary,the developed dc -ETM can learn meaningful and diverse topics at higher layers, indicating that more
[Figure] [Figure]
[Figure]
sure
[Figure]
deleted actually just
[Figure]
true
[Figure]
did
[Figure]
nd
[Figure]
1 st
[Figure]
Topic Embedding Visualization: After extracting hierarchical topic trees by dc -ETM, we visualizesome of these trees originated from different topic nodes at layer 5 by projecting their semanticembeddings with t-SNE [43].
As shown in Fig.
5(a), we can find that the topics in the same topictree tend to be closer than others from different trees in the semantic embedding space and similarphenomenon occurs in the words for describing the same root topic, which indicates the hierarchylearn by dc -ETM is of high quality.
Note that we also visualize the topics consisting of similar topwords in Fig. 5(b) and 5(c), learned by dc -ETM and SawETM respectively, which demonstrates thatthe developed dc -ETM can provide more meaningful and discriminative topic and word embeddings.
Conclusion
To provide higher-quality hierarchical latent representations for deep topic modeling, in this paper,with the deep-coupling generative process, we develop a novel dc -ETM, which is constructed byintroducing skip connections into the generative process of GBN and also incorporates both topicembedding and Weibull reparameterization techniques.
Utilizing the property of sequence-likegeneration process, we design a PG-based training algorithm for dc -ETM to further alleviate theinformation reduction at higher layers.
We note that the main idea of designing dc -ETM equippedwith the PG-based training algorithm can potentially be extended to other deep topic models.
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A Inference Network of dc -ETM
[Figure]
As a usual VAE-like model, the inference network (encoder) in dc -ETM is designed to approximatethe gamma distributed posteriors of latent representations of the corresponding generative model(decoder), specifically p ( θ n | x n ) defined in Eq.
As the gamma distribution, which can generatesparse and non-negative random variables, is not reparameterizable with respect to its shape parameter,we introduce the Weibull distribution [19] to construct our inference network due to its attractiveproperties as discussed in Appendix.
B, one of which is that a latent variable x ∼ Weibull ( k, λ ) canbe easily reparameterized as
[Equation]
Specifically, following traditional VAEs, we first factorize the variational posterior distributionq ( θ n | x n ) defined by the inference network in a hierarchical manner as follows
[Equation]
(18) which is expected to be flexible enough to well approximate the true posterior distribution p ( θ n | x n ) .
Then, for the design of the network structure, we develop a novel Weibull-based upward-downwardinference network, which contains both bottom-up deterministic and top-down stochastic paths,contributing to reducing the noise in the procedure of inferencing stochastic latent variables that arehigher in the hierarchy [15, 16].
As the inference network shown in the left part of 6, the bottom-up deterministic path takes the BoWvector x n as input to obtain hierarchical deterministic latent representations { h ( l ) n } Ll =1 as follows:
[Equation]
[Equation]
through specifically defining h n = MLP ( x n ) and h (1) n = MLP ( h n ) with MLP indicating a twolayered fully connected network and ⊕ being the concatenation operation at feature dimension.
Then,each deterministic latent representation h ( l ) n is further transferred into
[Equation] [Equation]
where Linear is a dense fully connected layer.
Finally, taking Φ ( l +1) θ ( l +1) n as the prior informationpassing from the deeper layer, the variational posterior q ( θ ( l ) n |− ) can be obtained with the stochastic up-down path as
[Equation]
where Softplus function is applied to ensure positive Weibull shape and scale parameters; ˆ k ( l ) n and ˆ λ ( l ) nincorporate the information passing from h ( l ) n ; and θ ( l ) n can be obtained with the reparameterizationtechnique defined in Eq.
We emphasize that the inference network of our dc -ETM can be naturally treated as an inverse procedure of the generation process of x n , whose generation is conditional on the latent representations{ θ ( l ) n } Ll =1 across all hidden layers.
Inverse to the generative network, the inference network candirectly inject the data information into the latent representation of each layer through h n and allowall the stochastic latent variables { θ ( l ) n } Ll =1 to have a deterministic dependency on the observationx n , empirically alleviating top stochastic latent variables from being collapsed [16].
B Properties of Weibull distribution•
Similar density characteristics with Gamma Distribution
The Weibull distribution has similar characteristics with gamma distribution, i.e. , the density functionsof the two distributions are quite similar
[Equation]
Gamma
[Equation]
• Easily Reparameterization
The latent variable x ∼ Weibull ( k, λ ) can be reparameterized as
[Equation]
24) leading to an expedient and numerically stable gradient calculation.
• Analytic KL-Divergence
The KL-divergence between the Weibull and gamma distributions has an analytic expression formulated as
[Equation]
C PG-based Training Algorithm
Modeling the generation of long sequence with RL-based methods has achieved great success, butstill suffers from high variance during model training.
The common ways to conduct policy gradientmethod for training include: i) employ the score-ratio gradient approximator like REINFORCE [44]to backward the gradients from Q-value Q π ( s ( l ) n , a ( l ) n ) to the parameters of the policy network π ; ii)introduce an additional “critic” network to estimate the Q-value Q π ( s ( l ) n , a ( l ) n ) as ˆ Q π ( s ( l ) n , a ( l ) n ) , thenbackward the gradients from estimated Q-value ˆ Q π ( s ( l ) n , a ( l ) n ) to the parameters of the policy networkπ through methods like deep deterministic policy gradient (DDPG) [32].
We emphasize that, bothmethods above are developed for the RL applications where the Q-value function is not differentiable,e.g., the reward r ( s ( l ) n , a ( l ) n ) at every time step l is given by the non-differentiable black-box environment simulator, or for the applications with hundreds or even thousands time steps, where the direct computation of the gradient from Q π ( s ( l ) n , a ( l ) n )
=
r ( s ( l ) n , a ( l ) n ) + E π (cid:104)(cid:80) l − 1 i =1 γ
i r ( s ( l − i ) n , a ( l − i ) n ) (cid:105) toπ is very expensive.
However, the REINFORCE estimator is notorious for its high variance, whichwould cause the learning of long sequence hard to converge, and the introducing of the “critic”network would also bring extra bias for the gradients and computation burden.
Moving beyond the gradient estimator of REINFORCE method or introducing the critic network,we design a more stable way to model the sequential generative process in deep topic model here.
Specifically, the number of time steps here is the number of layers L of the topic model, e.g., L is 5in this paper, which is a much smaller number compared to the hundreds of time step in other RLapplications.
Besides, we design the r ( s ( l ) n , a ( l ) n ) as a totally differentiable function in Eq.
14, whichleads to a differentiable Q-value function Q π ( s ( l ) n , a ( l ) n ) .
More specifically, recall the purpose ofpolicy gradient method is to maximize the Q-value by applying gradient descent to the policy modelπ , we develop the Policy Gradient based variational inference algorithm for dc -ETM in Algorithm 1.
Set minibatch size m and the number of Layer L ;
Initialize the encoder parameters Ω and decoder parameters Ψ ; for iter = 1 , 2 , · · · do
Randomly select a minibatch of m documents to form a subset X = { x j } 1 ,m ;
Draw random noise { ε li } m,Li =1 ,l =1 from uniform distribution; for Layer l = L, L − 1 , · · · , 1 do for i
=
l, l − 1 , · · · , 1 do
Calculate r ( s ( i ) n , a ( i ) n
D Datasets
R8 is a subset of 7,674 documents selected form 8 different review groups of the Reuters 21578dataset, and has been split into a training set of 5,485 documents and a testing set of 2,189 ones.20News dataset consists of 18,774 documents from 20 various new groups and has been split intoa training set of 11,314 documents and a testing set of 7,532 ones.
RCV1 is an archive of 804,114manually categorized newswire stories made available by Reuters.
WebKB is a dataset that includesweb pages from computer science departments of various universities, which has 4,518 web pagesthat are categorized into 6 imbalanced categories.
E Error Bars
We randomly run 5 seeds for our method in experiments and report the error bar as below.
[Table] [Table]
F Comparison of topic quality
To make an intuitive comparison on the aspect of topic quality, we visualize the 5-layer topics learnedby dc -ETM and Sawtooth on 20News dataset as shown in .
Obviously, the topics learned by Sawtoothare quite similar, which potentially explains the reason why concatenating its hierarchical latentdocument representations cannot improve and even hurt the performance on downstream tasks.
On thecontrary, the developed dc -ETM can learn meaningful and diverse topics in higher layers, indicatingthat more data information is passed to higher layers to alleviate the phenomenon of collapse.
[Figure]
G Limitation
The limitation could be the computation burden brought by the extra L − 1 training steps due to the
Policy Gradient training method.
However, since L is a small number, the extra computation burdenis affordable.
Besides, compared with the backbone GBN-based deep topic model, our method wouldnot bring extra computation burden during the testing stage, but leads to performance improvementsas shown in Section 5.
H Broader Impact
At first, we need to emphasize that this work is developed to mitigate the phenomenon of informationdecay in deep topic models, which has been widely disclosed in the topic modeling literaturebut few efforts have been made to address this challenge.
The main difficulty is the need forcarefully designing the probabilistic generative process to build the effective connection betweenthe observation and the corresponding latent representations, on the premise of preserving theinterpretable hierarchical topic modeling structure, rather than casually introducing skip-connections.
Utilizing the natural hierarchy in deep topic models, we provide a general solution equipped with anovel perspective to incorporate RL-based training algorithm, which brings quite a few contributingideas to this field.
Please note that, we take an NTM named SawETM as an example in this paper to illustrate how wedevelop a dc -ETM, but dc -ETM can be also applied to extend NTMs with similar structures.
Most ofexsiting NTMs adopt VAE-liked structures, but none of them attempt to solve the “ posterior collapse ”phenomenon in essence, resulting in that the latent representations at higher layers exhibit similar ormeaningless patterns (as shown in the Appendix F).
The significance of developing deep-coupling structure with RL-based training algorithm, which haseffectively improved the quality of the latent representations of a deep topic model at higher layers,goes beyond a single specific model.
I The implementation details of dc -ETM variants
To have an intuitive understanding about the implementation details of the projection metrics indc -ETM, we provide an overview of the network structure of a 3-layer dc -ETM in Fig.
As shownin Fig.
8, the topic matrix Φ ( l ) at layer l can be factorized into the product of two topic embeddingmatrices.
Then, there are two kinds of choices to obtain the projection matrices, leading to the variantsdistinguished by the suffix − α and − β .
Specifically, for the variant dc -ETM-
α defined in Eq.
(2), the projection matrix ˆ Φ (3) can be obtainedby multiplying Φ (1) Φ (2) Φ (3) , and then the augmented observation vector x (3) n can be generatedfrom the Poisson distribution with a rate of α (3) ˆ Φ (3) θ (3) n .
The other projection matrices ˆ Φ (2) andˆ Φ (1) can be obtained in a simliar way, where specifically defining ˆ Φ (1) :
=
Φ (1) .
For the variant dc -ETM- β defined in Eq.
(3), the projection matrix ˆ Φ (3) can be obtained by directlymultiplying β (0) T and β (3) , which is more efficient than dc -ETM- α .
Thus, in our consideration, witha shorter path for gradient propagation, the short connections in dc -ETM- β can perverse more datainformation than those in dc -ETM-
α , resulting in more informative latent document representationsto achieve better model performance with less “ posterior collapse ”.
We emphasize that, the projection in dc -ETM-
α could be applied to extend any existing deep topicmodels, while the projection in dc -ETM-
β is only applicable for the NTMs equipped with topicembedding techniques.(1) n  (2) n  (3) n  × (0) (0) T
K D    (3) (3) D K    (3) ˆ  × (0) (0) T
K D    (2) (2) D K    (2)
ˆ  × (0) (0) T
K D    (1) (1) D K    (1) ˆ  × (1) (1) T
K D    (2) (2) D K    (2)  × (3) (3)
D K    (2) (2) T
K D   
[Figure]
) n
[Figure]
2) n
[Figure]
3) n x
[Figure]
x
[Figure]
[Figure]
h (2) n h (3) n h
[Figure]
J Preliminary of “ posterior collapse ”J.1 Definition of “ posterior collapse ”
For a VAE-based model consisting of a decoder p θ ( x | z ) and an encoder q ϕ ( z | x ) , the definitionof posterior collapse is that the posterior of latent variables, denoted as q ϕ ( z | x ) , collapses to itprior p θ ( z ) , which is a non-informative distribution and independent of the data x .
It can bemathematically denoted as that the KL divergence between q ϕ ( z | x ) and p θ ( z ) is close to zero,represented as D KL [ q ϕ ( z | x ) || p ( z )] ≈ 0 .
J.2 How to measure “ posterior collapse ”
As the definition of “ posterior collapse ”, a promising metric to measure “ posterior collapse ” couldbe the KL-divergence between q ϕ ( z | x ) and p θ ( z ) , where a smaller KL-divergence score indicatesthat the posterior q ϕ ( z | x ) contains less data information and has a larger tendency to collapse to itsnon-informative prior.
In Table 5, we compare our method dc -ETM- β (policy) (for brevity, we noteit as " dc -ETM") with the previous SOTA NTM named SawETM with the metric of layer-wise KLdivergence on 20News, R8, and RCV1 datasets.
As the results shows, the KL divergence of SawETMgradually reduces to zero with the network going deeper, which indicates the occurrence of a seriousdegree of “ posterior collapse ” at higher layers and limited data information can be captured bythese latent variables at higher layers.
On the contrary, benefiting from the efficient skip connectionbetween the observation x n and multiple latent document representations { θ ( l ) n } Ll =1 , dc -ETM canachieve relatively larger KL-divergence scores at higher layers by alleviating “ posterior collapse ”,leading to more expressive latent document representations for downstream tasks.
KL-divergence of each layer
Metric
[Table]
J.3 Why “ posterior collapse ” in a VAE-based model
Besides the experimental analysis, to have a theoretical understanding of the reason why “posteriorcollapse” widely exists in the higher layers of hierarchical VAEs, we try to explain the phenomenonof “posterior collapse” from the perspective of information theory.
For ease of understanding, weuse a vanilla L -layer hierarchical VAE with a top-down inference network as an example, wheredc -ETMs can be all treated as VAE-based models.
Specifically, following [17], we extend theirtheoretical explanation for “posterior collapse” in a single-layer VAE to a multi-layer version.
Separating the latent variables as the lower-level variables z ≤ k = { z 1 , ..., z k } and the higher-levelones z >k = { z k +1 , ..., z L } , where k ∈ { 0 , ..., L − 1 } , then the ELBO for this hierarchical VAE canbe reformulated as
[Equation]
(26) where q ϕ ( z L | z L +1 ) :
= q ϕ ( z L | x ) , p θ ( z L | z L +1 ) :
= p θ ( z L ) , and the main contribution to the expected log-likelihood term is coming from the lower-level latent variables z ≤ k before the k -th hiddenlayer [16].
Once the generation capacity of the generative model p θ ( x | z ≤ k ) is powerful enough toreconstruct the observation x well, the variational posteriors of higher-level latent variables z >kwill be optimized to be close to their priors, i.e., q ϕ ( z >k | x )
≈ p
θ ( z >k ) , leading the representationslearned by VAE at higher layers to be meaningless and cannot provide faithful summaries for x ,which is well-known as the phenomenon of “ posterior collapse ” or “ latent variable collapse ” [17].
To find the potential solutions to alleviating “ posterior collapse ”, in the following, we reinterpretthis phenomenon from the perspective of information theory by extending the findings in [17] to a hierarchical VAE scenario.
For ease of undertanding, we define the mutual information between thedata x and the higher-level latent variables z >k as
[Equation]
) , which is induced by the variational posterior q ϕ ( z >k | x ) .
Then KL term in Eq.
(26) can be rewrittenas
[Equation]
where q ϕ ( z >k ) =
E p ( x ) [ q ϕ ( z >k | x )] .
By substituting Eq.
(27) into Eq.
(26), due to the non-negativityof mutual information and KL divergence, we can find that maximizing the ELBO is oppositeto maximizing the mutual information I q ( x , z >k ) .
When I q ( x , z >k ) is minimized to zero, thevariational posterior q ϕ ( z >k | x ) will be independent of the data x , which leads to the phenomenon of“ posterior collapse ”.
Thus, exploiting the property of the deep NTMs and building the skip connection between the x nand θ ( l ) n with two projection variants, as shown in Fig. 8, is promising to force the higher layers oflatent variables be more informative by maximizing the mutual information.
K Evaluations of topic quality under more metrics
For evaluating the quality of the learned topics, we introduce more metrics for comparison between
SawETM and dc -ETM- β (policy) (for brevity, we note it as " dc -ETM") including: topic diversity,topic coherence, and topic quality (a product of the topic diversity and topic coherence).
As shown in
Table 6, Table 7, and Table 8, dc -ETM can achieve comparable performance on the aspect of topicquality at the first layer while significantly outperform SawETM in the higher layers.
[Table]
20News
[Table] [Table]
L Layer-wise comparison under the clustering task
We provide an additional evaluation for the clustering tasks on each layer of SawETM and dc -ETMβ (policy) (for brevity, we note it as " dc -ETM").
As shown in Table 9, the higher layer θ ( l ) n of
SawETM can only preserve limited data information for the downstream document clustering task,which could explain why the SawETM cannot obtain a gain of performance improvement even ifconcatenating all hidden layers, specifically { θ ( l ) n } Ll =1 (denoted as "All")
[Table]
[Table]