Year: 2021

Abstract:
Beyond the success story of pre-trained language models (PrLMs) in recent natu1 ral language processing, they are susceptible to over-fitting due to unusual large 2 model size. To this end, dropout serves as a therapy. However, existing methods 3 like random-based, knowledge-based and search-based dropout are more general 4 but less effective onto self-attention based models, which are broadly chosen as 5 the fundamental architecture of PrLMs. In this paper, we propose a novel dropout 6 method named AttendOut to let self-attention empowered PrLMs capable of more 7 robust task-specific tuning. We demonstrate that state-of-the-art models with elab8 orate training design may achieve much stronger results. We verify the universal9 ity of our approach on extensive natural language processing tasks. 10

Beyond the success story of pre-trained language models (PrLMs) in recent natu-1 ral language processing, they are susceptible to over-fitting due to unusual large2 model size. To this end, dropout serves as a therapy. However, existing methods3 like random-based, knowledge-based and search-based dropout are more general4 but less effective onto self-attention based models, which are broadly chosen as5 the fundamental architecture of PrLMs. In this paper, we propose a novel dropout6 method named AttendOut to let self-attention empowered PrLMs capable of more7 robust task-specific tuning. We demonstrate that state-of-the-art models with elab-8 orate training design may achieve much stronger results. We verify the universal-9 ity of our approach on extensive natural language processing tasks.10
1 Introduction11
Self-attention network (SAN) empowered models like Transformer [1] have achieved remarkable12 success in recent natural language processing, which have been broadly chosen as basic architec-13 ture in a series successful pre-trained language models (PrLMs) such as BERT [2], RoBERTa [3],14 ALBERT [4], ELECTRA [5], DeBERTa [6] and GPT [7].15
SAN has drawn a great deal of curiosity on its conceptually simple but powerful attention mecha-16 nism. However, SAN still remains a black box and more and more works attempt to unveil its inner17 principle, where the biggest mystery lies in its attention matrix. Our work is inspired by several18 recent discoveries which turn our views up and down. [8, 9] show that fixed Gaussian or even ran-19 dom alignment attention matrix may rival standard SAN, while more recently, [10, 11] prove that20 SAN may encounter a rank collapse with deepening of layers. A more concrete explanation is in-21 formation diffusion [12], which states that the input vectors are progressively assimilating through22 continuously making self-attention. We attribute these problems to the sever co-adaption [13] be-23 tween attention elements, a form of over-fitting onto SAN. As a result, self-attention empowered24 PrLMs hardly bring into their full play, especially for the fine-tuning stage, where task-specific data25 is always with limited capacity.26
Dropout [13] serves as a therapy to deal with the problem, by randomly shutting down a set of units27 during training stage. When specified on self-attention, dropout is equivalent to adding attention28 mask to the attention matrix. However, random-based dropout methods like vanilla Dropout [13] or29 DropConnect [14] are all subject to a pre-defined distribution like Bernoulli or Gaussian, longing for30 exhaustive grid search for an optimal probability. Thereby a variety of works attempt to utilize man-31 ual attention mask to obtain a more informative attention matrix [15, 16], whereas all these methods32 require prior knowledge on model or data, which could be costly or unavailable. More recently, the33 rise of Neural Architecture Search [17, 18] gives birth to search-based dropout [19], which automat-34 ically chooses an optimal dropout pattern based on additional validation performances. However,35 the huge search space brings heavy consumption and more importantly, the obtained dropout pattern36 is still fixed with a pre-defined probability, which is static and sample-independent, ignoring the37 dynamics within different samples. In this paper, we focus on task-specific tuning of self-attention38
Submitted to 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Do not distribute.
empowered PrLMs and propose a novel dropout method named AttendOut onto attention layers,39 which leverages self-attention to dynamically generate dropout patterns for each attention layer as40 well as each sample through an end-to-end manner. We demonstrate that the previous state-of-the-art41 models with elaborate training design may achieve much stronger results. We verify the universality42 of our approach on extensive natural language processing tasks. Guided by AttendOut, we pro-43 pose another two attention regularizers to enable simple but effective performance boost with no44 additional cost.45
2 Related Work46
Dropout is proposed to alleviate over-fitting problem in DNNs. Apart from vanilla Dropout [13]47 and DropConnect [14] which randomly shut down a subset of activations or hidden weights, there48 are a variety of dropout methods proposed, e.g. Alpha Dropout [20], Variational Dropout [21, 22],49 Adversarial Dropout [23], Energy-based Dropout [24]. However, random-based dropout encounters50 slow experiment cycle due to inevitable grid search. Inspired by Neural Architecture Search [17, 18],51 [19] proposes AutoDropout to automate the process of designing dropout patterns. A similar line of52 work is dynamic tuning of dropout, which further allows adaptive dropout probabilities under differ-53 ent training moments. [25] proposes Concrete Dropout with continuous relaxation under Concrete54 distribution, [26] proposes Learnable Bernoulli Dropout under discrete Bernoulli distribution using55 Augment-REINFORCE-Merge estimator [27], while [28] proposes Context Dropout by optimizing56 the evidence lower bound.57
With self-attention network continuously stands out, dropout is being explored onto self-attention58 based models. LayerDrop [29] randomly removes entire SAN blocks, while DropHead [30], Head-59 Mask [31] randomly remove certain attention heads. UniDrop [32] unifies these dropout methods,60 which facilitates text classification and machine translation tasks. Additionally, prior knowledge is61 shown highly effective for guiding attention dropout as in SG-Net [15] and SIT [16], which inten-62 tionally discard syntax-unrelated attention units with the help of structural clues.63
3 Preliminaries64
In this section, we provide the preliminaries for the proposed approach. We first review the details65 of self-attention proposed in [1]. Based on the specific architecture, we elaborate the concerned66 attention dropout.67
3.1 Self-Attention68
Generally, a standard SAN block is mainly composed of an attention layer and several feed-forward69 layers (actually there are residual connection, layer normalization, etc. as well). The input of it is a70 sentence or batch of sentences of length n, which is first embedded through an embedding layer. The71 embedded input E may go through three linear projections WQ, WK and WV referring to query, key72
and value layers respectively, and then obtain three matrices Q, K and V referring to the query, key73 and value components of self-attention. Subsequently, a dot-product of Q and K is taken and then74 normalized using Softmax function to obtain the attention matrix A. Then another dot-product of75 A and V follows. The mentioned calculation can be formalized as follow:76
Attention(Q,K, V ) = Softmax ( Q ·KT√
dk
) · V (1)
where √ dk is a scaling factor. Finally, the self-attention layer ends up with a linear projection WO77 to output.78
During the aforementioned process, we highlight a key phases, that is the attention matrix A, which79 is a dot-product of n×n from two separate linear projections WQ and WK . A is viewed as a feature80 map which stores the node-to-node significance in different scores. Various works show that there81 hides implicit but highly needed semantic clues.82
3.2 Dropout on Self-Attention83
Our dropout will apply to the attention matrix of the concerned attention layer. We first define two84 specific dropouts onto Eq. 1, where both implementations are just as simple as in standard dropout85 via a mask matrix M .86
Weights Dropout. Weights dropout is applied to the attention matrix after Softmax function by87 default, which is formulated as:88
Attention(Q,K, V ) = ( Softmax ( Q ·KT√
dk
) ⊙M ) · V (2)
where M is a binary matrix with elements in {0, 1} and ⊙ refers to element-wise multiplication.89
Scores Dropout. Different from weights dropout, scores dropout is applied before Softmax func-90 tion, which is formulated as:91
Attention(Q,K, V ) = Softmax ( Q ·KT√
dk +M
) · V (3)
Since the outer Softmax, we conduct an addition instead of multiplication, where elements in M92 are set to 0 for kept units and −inf for removed ones. Note that the Softmax takes a similar93 function as the scaling factor of 1/p in vanilla Dropout [13], which balances the expectation of the94 network.95
Weights dropout is commonly used in self-attention based models, while scores dropout is less96 explored, which is our focus in this paper. For scores dropout, we need to pay attention to a special97 case, when all attentions are shut down, that is, all elements in M equal to −inf at the same time.98 Such case can be formulated as follow:99
Attention(Q,K, V ) = Softmax (M) · V (4) Note that Softmax (M) obtains to a constant matrix, where each unit equals to 1/n. In this case,100 the attention matrix is fixed and consequently the WQ, WK and dot-product in between are skipped.101
4 Methodology102
In this paper, we propose Attention differentiable dropOut (AttendOut), which contributes technique103 novelty in the following way: (1) dynamic and task-specific tuned; (2) end-to-end trained; (3) gra-104 dient optimized dropout method onto self-attention empowered PrLMs. We elaborate our approach105 with two parts, in which the first is composition, while the second is training algorithm.106
4.1 Elements of AttendOut107
Our training architecture is composed of three modules, A-Net (Attacker), D-Net (Defender) and108 G-Net (Generator). D-Net and A-Net are two identical models and trained simultaneously through109 standard gradient descent, while G-Net is a learnable dropout maker and trained through policy110 gradient. Now we elaborate each of them.111
Defender - Attacker As suggested, defender and attacker are two competitors playing a game112 with each other on specific criteria, e.g. training accuracy, training loss. Specifically, D-Net and A-113 Net are two identical self-attention empowered PrLMs, e.g. BERT, RoBERTa. However, they follow114 different dropout strategies. D-Net receives regular dropout as default in specific models, while A-115 Net receives additional dropout decision from G-Net onto its corresponding attention layers.116
Generator G-Net acts as a dropout maker through generating a mask matrix for each attention117 layer during training stage. As aforementioned, the common dropout strategies rely on randomness,118 which intends to shut down the co-adaption but not powerful enough. However, our dropout maker119 is an agent which is able to intelligently choose and learn dropout patterns for each sample. Specif-120 ically, after training for a fixed number of steps, we conduct evaluation for both A-Net and D-Net.121 When A-Net obtains a higher score than D-Net, which means attacker wins the game, G-Net will be122 rewarded positively. When defender wins, G-Net will be punished with a negative reward. In con-123 sequence, G-Net learns appropriate dropout patterns through the game between D-Net and A-Net,124 while assisting A-Net to win the game. On the other hand, A-Net needs to be stronger when training125 under such powerful dropout, which makes it much more robust from over-fitting. Compared to126 search-based dropout, G-Net is triggered by the difference between two model derivatives with and127 without dropout, instead of the final feedback on validation set, which makes it end-to-end-possible128 and sample-dependent.129
The design of G-Net is the most delicate part, which is also a self-attention based model with iden-130 tical number of layers with D-Net and A-Net. However, we make several improvements. 1) G-Net131 only exports the attention scores from attention layers with no extra output layers, from which we132 apply Gumbel [33, 34] to sample the actions to obtain the dropout mask. 2) G-Net only makes133 one-head attention and share one group of parameters for all attention layers. 3) G-Net is excluded134 of feed-forward layers, which may obscure the impact of self-attention [11, 10].135
4.2 Training with AttendOut136
The core of training with AttendOut is to find a way to optimize G-Net, which receives signals from137 the difference between D-Net and A-Net. Supposing there is a list of dropout actions by G-Net:138
a1:T = {a1, a2, a3, · · · , aT }
where T refers to the number of samples, for each action at, G-Net may achieve a reward rt. The139 optimization objective is to maximize the overall rewards of list a1:T , denoted as R, that is:140
J(θG) = EP (a1:T ;θG)[R] where R = ∑T
t=1 rt. Since R is non-differentiable, we use policy gradient to update θG as in [17]:141
∇θGJ(θG) = T∑
t=1
EP (a1:T ;θG)[∇θG logP (at|a(t−1):1; θG)rt]
The above equation could be approximated as:142
1
m m∑ k=1 T∑ t=1 ∇θG logP (at|a(t−1):1; θG)rt
For a model with n attention layers, each dropout decision is composed of n inner decisions of each143 layer. Additionally, each attention layer contains an attention matrix of l × l, namely l2 elements144 dropped or kept. Thus, we denote a dropout unit as dij , where i refers to the ith layer while j refers145 to the jth element of the attention matrix.146
However, nl2 dropout units bring a huge space, which makes it impossible to calculate the joint prob-147 ability. To this end, we introduce the independence assumption that each dropout unit is independent148 with each other. Under the relaxation, we can make the following probability likelihood:149
logP (at|a(t−1):1; θG) = 1
nl2 ∑ i,j logP (dijt |d ij (t−1):1; θG)
where the summation ∑n
i=1 ∑l2 j=1 is briefly denoted as ∑ i,j .150
Thus, the final gradient could be formalized as:151
∇θGJ(θG) = 1
m
1
nl2 m∑ k=1 T∑ t=1 ∑ i,j ∇θG logP (d ij t |d ij (t−1):1; θG)(rt − b) (5)
where b is a baseline function of moving average [35]. Note that we do not apply additional regular-152 izers like L0 and L1 penalty, which impose unnecessary bias.153
Algorithm 1 summarizes the overall procedure of training PrLMs with AttendOut. We first initialize154 all three networks. Note that D-Net and A-Net should be kept identical at the beginning of each155 training step. A straightforward strategy is to choose the better one to cover the other. To add156 randomness, we sample from D-Net and A-Net based on their evaluation performances, with higher157 probability for the better one. Then for each step, D-Net and A-Net are fed with the same mini-batch158 data and updated via standard gradient descent, meanwhile each batch will be cached. After training159 for T steps, which we denote as a dropout step, both D-Net and A-Net are evaluated on additional160 validation samples, which could be development set data, noisy training data or a small split of train-161 ing data. In this paper, we simply use development set. For efficiency, we make random sampling162 on it to retrieve T samples for evaluation. Based on the evaluation scores, G-Net is rewarded with163 {r1, r2, r3, · · · , rT } and updated via Eq. 5. At the end of each dropout step, the cached samples164 will be released and D-Net and A-Net will be re-initialized.165
Resource Usage We notice that training PrLMs with AttendOut may sacrifice time and memory167 cost. The detailed resource usage is shown in Appendix. Taking RoBERTa as an example, the168 algorithm requires two RoBERTa models as well as a smaller self-attention based generator, which169 is 1/3 of RoBERTa size. Considering cached samples, roughly speaking, AttendOut requires twice170 graphic memory as well as twice training time compared to a single model, which is a middle speed171 line between random-based dropout and neural architecture search (Dropout [13] < AttendOut≪172 AutoDropout [19]). However, AttendOut contributes to remarkable performance gain compared to173 other attention dropout methods.174
Pre-training Our approach is both feasible for both fine-tuning and pre-training stage of PrLMs175 but expensive for the latter. However, we try to serve for the most delicate part of concerned issue,176 since pre-training is generally done on large-scale data with modest training epochs, which makes it177 less susceptible from over-fitting.178
We demonstrate the universal effectiveness of AttendOut on extensive natural language processing180 tasks. For all mentioned tasks, we apply our method on BERT [2] and its stronger variant RoBERTa181 [3]. Our implementations are based on PyTorch using transformers [36]. For further training details,182 please refer to Appendix.183
Our experiments include: (1) natural language understanding: General Language Understanding184 Evaluation (GLUE) benchmark [37], a collection of nine natural language understanding tasks (here185 we experiment on five of them, SST-2, MRPC, QNLI, MNLI-mm and CoLA; (2) document clas-186 sification: IMDB [38], a sentiment analysis dataset where about 15% of the documents are longer187 than 512 word-pieces; (3) named entity recognition: CoNLL2003 [39]; (4) part-of-speech tag-188 ging: English Penn Treebank (PTB) [40]; (5) multiple choices question answering: SWAG [41].189 We report both test and development results for GLUE sub-tasks since the large bias between them,190 while development results only for all the other tasks.191
Note that we only adjust the dropout steps and keep all other parameters the same for strict fair192 comparison. For example, the parameters we use in RoBERTa are identical with what we use in193 training with AttendOut including both D-Net and A-Net.194
6 Results195
6.1 Significance Analysis196
Pictorially in Table 1, RoBERTa is strong enough as it outperforms BERT by a big margin, while197 AttendOut empowered RoBERTa still outperforms it on all five GLUE sub-tasks. For small-scale198 datasets, which are more likely to over-fit, AttendOut helps unfold remarkable performance gain199 (12.1% / 3.5% over BERT on CoLA, 1.7% / 1.4% over BERT on MRPC). However, for large-200 scale one like MNLI, which tends to be more stable, AttendOut still produces considerable boost,201 (1.4% / 1.4% over RoBERTa, 1.1% / 0.7% over BERT).202
Furthermore, AttendOut is shown universally effective as in Table 2. For POS Tagging, BERT203 and RoBERTa have achieved very strong baselines, while AttendOut empowered ones are even204 stronger, (1.1% over BERT on PTB). Similar results are seen on document classification and NER.205 For SWAG, however, AttendOut seems weakly effective (0.6% over BERT, 0.4% over RoBERTa).206
6.2 Visual Analysis207
Dropout Patterns Another concerned issue is the dropout proportions by AttendOut. Figure 3208 depicts the patterns on several datasets. We may find several interesting phenomenons. First, the209 overall patterns largely differ from datasets, which is fair since AttendOut is sample-dependent.210 However, we may observe something in common. Overall, the lower layers take higher dropout211 probabilities. For example on QNLI, the first layer almost remains steady with the probability of212 0.55 during the training process, while the fourth one continuously decays in a higher rate. Intu-213 itively, the first three layers undertake a similar trend in each dataset, while there might be an up and214 down for the fourth one as in SST-2 and CoLA. Especially for CoLA, we see unusual high dropout215 probabilities in the final period (around 0.9), which are close to complete dropout. We notice that216 CoLA is a small set with 8500 training samples, on which SAN model is more inclined to suffer217 from over-fitting. Therefore, PrLM on CoLA encounters more intensive dropout through AttendOut.218
Convergence Figure 4 depicts the accuracy trends of RoBERTa on SST-2, QNLI, MNLI respec-219 tively. Due to a stronger dropout module, the one with AttendOut tends to fall behind (SST-2,220 MNLI) at the beginning of training. However, model becomes stronger since the second epoch221 (SST-2, QNLI). Especially on MNLI, RoBERTa obtains better results in the first two epochs and it222 drops in the last one, while with AttendOut, the performance is steadily rising for all three epochs.223
7 Ablation Study224
In this section, we conduct further experiments to demonstrate the effectiveness of AttendOut. Due225 to space limitation, we conduct corresponding experiments on development sets only.226
7.1 Attention Dropout227
Vanilla Dropout We conduct comparison with vanilla Dropout [13], in which we dropout the228 attention matrix for all layers with Bernoulli distribution of p. Here, we choose the dropout proba-229 bilities in {0.1, 0.2}.230
LayerDrop We also compare with LayerDrop [29], which focuses on skipping the entire encoder231 blocks, Inspired of it, we design another strategy which randomly skips attention layers via Eq. 4.232 For fair enough comparison, we set the dropout probabilities to 0.2 for both methods, following the233 settings in [29].234
Intuitively in Table 3, vanilla Dropout with fixed probability does not produce noticeable gain (1.9%235 bellow RoBERTa on CoLA). However, AttendOut shows powerful advantage (4.1%, 1.0% and 1.0%236 over vanilla Dropout on CoLA, QNLI and MNLI), which stresses the necessity of dynamic dropout237 patterns rather than fixed static one. On the other hand, both layer-level regularizers are effective,238 while attention LayerDrop performs stronger and more stable on all the three. Especially on CoLA,239 it outperforms RoBERTa by 1.7 points, while LayerDrop meets a performance drop, which demon-240 strates that removing the attention layers act as a more effective regularizer than removing the entire241 SAN block as for self-attention based models.242
7.2 Pattern Approximation243
Guided by AttendOut, we design a dropout scheduler, in which we utilize piece-wise linearity to244 approximate the real curves as depicted in Figure 3. Taking QNLI as an example, we initialize245 the dropout probabilities to 0.6 for all attention layers and set a a specific slope for each of them.246 Note that here the corresponding mask matrices are randomly-generated and subject to Bernoulli247 distribution. In AttendOut, however, the distribution are learned dynamically through self-attention248 of G-Net.249
As shown in Table 4, RoBERTa with scheduled Bernoulli dropout works surprisingly well on both250 CoLA and QNLI, which outperforms RoBERTa by 0.8 and 0.6 points respectively, closer to At-251 tendOut, even if the strategy here is random-based and much looser. The guided scheduled dropout252 helps unfold the correctness of the dynamic dropout patterns learned by AttendOut as well as the253 self-attention based dropout maker.254
8 Conclusion255
This paper focuses on the co-adaption problem of deep self-attention networks, and presents a novel256 dropout method onto self-attention empowered pre-trained language models. Extensive experiments257 on multiple natural language processing tasks demonstrate that our proposed approach is universal258 and qualified to enable more robust task-specific tuning, which contributes to much stronger state-259 of-the-arts. We probe into the learned dropout patterns on different tasks, which empirically guide260 us to the very needed dynamic attention dropout design.261
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Review summaries:
This paper points out that the self-attention network can easily suffer from the co-adaption problem. To address the co-adaption problem, this paper introduces a novel dropout method, named Attention differentiable dropOut, for robust task-specific tuning. Experimental results also demonstrate the effectiveness of the proposed methods. 

This paper proposes a novel dropout strategy for self-attention, namely AttendOut. AttendOut trains 3 independent networks.  - Network G generates a $nxn$ dropout mask for the self-attention matrix for each layer conditioned on the input data.   - Network D is trained by applying the dropout mask generated by G - Network A is trained by applying the random dropout mask over self-attention.  Network D and A are trained with the task-specific objective. Importantly, the network G is trained with a reward defined by the advantage of D over A and optimized by the policy gradient.   Empirically, the proposed shows clear gains under language pretraining and fine-tuning.

This work proposes a new adaptive dropout technique for large self-attention pre-trained language models. Their method, called AttendOut has three main elements, an Attacker, a Defender and a Generator. The Generator is trained through policy gradient to (adaptively) generate the dropout which is used in the Attacker network. The Generator network will get positive rewards when the Attacker network performs better than the Defender network in an evaluation phase. The authors test their method on a subset of tasks from GLUE, document classification, named entity recognition, POS tagging and multiple choice question answering. Their method, which could be applied universally, achieves better results (compared to vanilla models) on the aforementioned tasks when applied on BERT and RoBERTa models.