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15_6 |
Automatic Speech Recognition System Models
An end-to-end ASR model maps input feature vectors to an output sequence of vectors of posterior probabilities of tokens without using separate acoustic model, pronunciation model and language model. In this work we implemented two different types of state of art end to end... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 6 | 3,553 | 4,123 |
15_7 |
Connectionist Temporal Classification (CTC)
The main ideas behind CTC based ASR were first introduced in the following papers BIBREF8 , BIBREF9 . In our work we used a single layer gated recurrent unit (GRU) BIBREF10 with 128 hidden units as encoder for the CTC network. The decoder consists of a combination of a den... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 7 | 4,123 | 4,578 |
15_8 | The number of time steps of the GRU encoder is equal to product of the sampling frequency of the input features and the length of the input sequence. Since different speakers have different rate of speech, we used dynamic recurrent neural network (RNN) cell. There is no fixed value for time steps of the encoder.
Usua... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 8 | 4,578 | 5,034 |
15_9 | A RNN based CTC network tries to make length of output tokens equal to T by allowing the repetition of output prediction unit tokens and by introducing a special token called blank token BIBREF8 across all the frames. We used CTC loss function with adam optimizer BIBREF11 and during inference time we used CTC beam sea... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 9 | 5,034 | 5,648 |
15_10 | Let the number of time steps of the RNN encoder for ( INLINEFORM6 , INLINEFORM7 ) is INLINEFORM8 . In case of character based CTC model, the RNN predicts a character at every time step. Whereas in word based CTC model, the RNN predicts a word at every time step. For the sake of simplicity, let us assume that length of... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 10 | 5,648 | 6,021 |
15_11 | Let the probability vector output by the RNN at each time step INLINEFORM11 be INLINEFORM12 and let INLINEFORM13 value of INLINEFORM14 be denoted by INLINEFORM15 . The probability that model outputs INLINEFORM16 on input INLINEFORM17 is given by INLINEFORM18 . During the training phase, we would like to maximize the c... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 11 | 6,021 | 6,433 |
15_12 |
In case when the length of INLINEFORM0 is less than INLINEFORM1 , we extend the target vector INLINEFORM2 by repeating a few of its values and by introducing blank token ( INLINEFORM3 ) to create a target vector of length INLINEFORM4 . Let the possible extensions of INLINEFORM5 be denoted by INLINEFORM6 . For example... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 12 | 6,433 | 6,923 |
15_13 | We then define INLINEFORM16 as INLINEFORM17 .
In our work we used character based CTC ASR model. CTC assumes the conditional independence constraint that output predictions are independent given the entire input sequence.
RNN Encoder-Decoder or Attention model
RNN encoder - decoder ASR model consists of a RNN encod... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 13 | 6,923 | 7,463 |
15_14 | There is no fixed value for time steps in our case. We used dynamic RNN cell. We used a single layer GRU with 128 hidden units for both encoder and decoder. A dense layer followed by softmax activation is used after the decoder GRU to get the prediction probabilities. Dense layer performs an affine transformation. The... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 14 | 7,463 | 7,908 |
15_15 | Training objective is to maximize the log probability of the ordered conditionals, ie: INLINEFORM0 , where X is input feature vector, INLINEFORM1 's are the labels for the ordered words present in that training example and INLINEFORM2 is the length of the output label sentence for that example. Cross entropy was used ... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 15 | 7,908 | 8,532 |
15_16 | Let the number of times steps of encoder GRU for that example be INLINEFORM2 . The GRU encoder will transform the input features ( INLINEFORM3 ) into hidden output feature vectors ( INLINEFORM4 ). Let INLINEFORM5 word label in INLINEFORM6 (sentence) be INLINEFORM7 , then to predict INLINEFORM8 at decoder time step INL... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 16 | 8,532 | 9,064 |
15_17 |
INLINEFORM0 can be intuitively seen as a measure of how much attention INLINEFORM1 must pay to INLINEFORM2 , INLINEFORM3 . INLINEFORM4 is mathematically defined as INLINEFORM5 , where INLINEFORM6 is hidden state of the decoder GRU at time step INLINEFORM7 .
The way of computing value for INLINEFORM0 depends on the ... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 17 | 9,064 | 9,608 |
15_18 |
Design of Experiments for building the database
We built two types of simultaneous speech EEG recording databases for this work. For database A five female and five male subjects took part in the experiment. For database B five male and three female subjects took part in the experiment. Except two subjects, rest all... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 18 | 9,608 | 10,240 |
15_19 | This data was recorded in presence of background noise of 40 dB (noise generated by room air conditioner fan). We then asked each subject to repeat the same experiment two more times, thus we had 30 speech EEG recording examples for each sentence.
For data set B, the 8 subjects were asked to repeat the same previous ... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 19 | 10,240 | 10,789 |
15_20 | Our EEG cap had 32 wet EEG electrodes including one electrode as ground as shown in Figure 1. We used EEGLab BIBREF17 to obtain the EEG sensor location mapping. It is based on standard 10-20 EEG sensor placement method for 32 electrodes.
For data set A, we used data from first 8 subjects for training the model, remai... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 20 | 10,789 | 11,319 |
15_21 |
EEG and Speech feature extraction details
EEG signals were sampled at 1000Hz and a fourth order IIR band pass filter with cut off frequencies 0.1Hz and 70Hz was applied. A notch filter with cut off frequency 60 Hz was used to remove the power line noise. EEGlab's BIBREF17 Independent component analysis (ICA) toolbox... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 21 | 11,319 | 11,799 |
15_22 | We extracted five statistical features for EEG, namely root mean square, zero crossing rate,moving window average,kurtosis and power spectral entropy BIBREF0 . So in total we extracted 31(channels) X 5 or 155 features for EEG signals.The EEG features were extracted at a sampling frequency of 100Hz for each EEG channel... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 22 | 11,799 | 12,308 |
15_23 | Similarly zero crossing rate was chosen as it is a commonly used feature both for speech recognition and bio signal analysis. Remaining features were chosen to capture time domain statistical information. We performed lot of experiments to identify this set of features. Initially we used only spectral entropy and zero... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 23 | 12,308 | 12,909 |
15_24 | We first extracted MFCC 13 features and then computed first and second order differentials (delta and delta-delta) thus having total MFCC 39 features. The MFCC features were also sampled at 100Hz same as the sampling frequency of EEG features to avoid seq2seq problem.
EEG Feature Dimension Reduction Algorithm Details... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 24 | 12,909 | 13,465 |
15_25 | We reduced the 155 EEG features to a dimension of 30 by applying Kernel Principle Component Analysis (KPCA) BIBREF19 .We plotted cumulative explained variance versus number of components to identify the right feature dimension as shown in Figure 2. We used KPCA with polynomial kernel of degree 3 BIBREF0 . We further c... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 25 | 13,465 | 13,925 |
15_26 |
When we used the EEG features for ASR without dimension reduction, the ASR performance went down by 40 %. The non linear dimension reduction of EEG features significantly improved the performance of ASR.
Results
The attention model was predicting a word and CTC model was predicting a character at every time step, h... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 26 | 13,925 | 14,417 |
15_27 |
Table i@ and ii@ shows the test time results for attention model for both the data sets when trained using EEG features and concatenation of EEG, acoustic features respectively. As seen from the results the attention model gave lower WER when trained and tested on smaller number of sentences. As the vocabulary size i... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 27 | 14,417 | 15,039 |
15_28 |
Table iv@ and v@ shows the results obtained using CTC model. The error rates for CTC model also went up with the increase in vocabulary size for both the data sets. However the CTC model was trained for 500 epochs compared to 100 epochs for attention model to observe loss convergence and batch size was set to one for... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 28 | 15,039 | 15,555 |
15_29 | Table vi@ shows the CTC model test time results when we trained the model using EEG features from only T7 and T8 sensors on the most noisy data set B. We observed that as vocabulary size increase, error rates were slightly lower than the error rates from Table iv@ where we used EEG features from all 31 sensors with di... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 29 | 15,555 | 16,121 |
15_30 |
Figures 4 shows the visualization of the attention weights when the attention model was trained and tested using only EEG features for Data set B. The plots shows the EEG feature importance ( attention) distribution across time steps for predicting first sentence and it indicates that attention model was not able to ... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 30 | 16,121 | 16,843 |
15_31 |
For both attention and CTC model we observed that as the vocabulary size increase, concatenating acoustic features with EEG features will help in reducing the test time error rates.
We further plan to publish our speech EEG data base used in this work to help advancement of research in this area.
For future work, w... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 31 | 16,843 | 17,304 |
15_32 |
We will also investigate whether it is possible to improve the attention model results by tuning hyper parameters to improve the model's ability to condition on the input,improve CTC model results by training with more number of examples and by using external language model during inference time.
Acknowledgement
We... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 32 | 17,304 | 17,870 |
15_33 | Training loss convergence for CTC model using only EEG features for first 3 sentences from data set B
TABLE I WER ON TEST SET FOR ATTENTION MODEL FOR DATA SET A
Fig. 3. Training loss convergence for attention model using only EEG features for first 10 sentences from data set A
TABLE II WER ON TEST SET FOR ATTENTION... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 33 | 17,870 | 18,332 |
15_34 | Visualization of attention weights for the first sentence
TABLE IV CER ON TEST SET FOR CTC MODEL FOR DATA SET B
TABLE V CER ON TEST SET FOR CTC MODEL FOR DATA SET A
TABLE VI CER ON TEST SET FOR CTC MODEL FOR DATA SET B USING EEG FEATURES FROM ONLY T7 AND T8 ELECTRODES
TABLE VII CER ON TEST SET FOR CTC MODEL FOR DA... | https://arxiv.org/abs/1906.08871 | Advancing Speech Recognition With No Speech Or With Noisy Speech | 34 | 18,332 | 18,811 |
16_0 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks. However, a major blocking issue of applying BERT to online services is that... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 0 | 0 | 716 |
16_1 | In this paper, we address this issue by proposing a hybrid solution named LadaBERT (Lightweight adaptation of BERT through hybrid model compression), which combines the advantages of different model compression methods, including weight pruning, matrix factorization and knowledge distillation. LadaBERT achieves state-... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 1 | 716 | 1,300 |
16_2 | Ideally, people can start from a pre-trained BERT checkpoint and fine-tune it on a specific downstream task. However, the original BERT models are memory-exhaustive and latency-prohibitive to be served in embedded devices or CPU-based online environments. As the memory and latency constraints vary in different scenari... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 2 | 1,300 | 1,967 |
16_3 | For example, DistilBERT BIBREF2 is re-trained on the same corpus as pre-training a vanilla BERT from scratch; and TinyBERT BIBREF3 utilizes expensive data augmentation to fit the distillation target. The costs of these model compression methods are as large as pre-training and unaffordable for low-resource settings. T... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 3 | 1,967 | 2,604 |
16_4 | Specifically, LadaBERT is based on an iterative hybrid model compression framework consisting of weighting pruning, matrix factorization and knowledge distillation. Initially, the architecture and weights of student model are inherited from the BERT teacher. In each iteration, the student model is first compressed by ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 4 | 2,604 | 3,299 |
16_5 |
We conduct extensive experiments on five public datasets of natural language understanding. As an example, the performance comparison of LadaBERT and state-of-the-art models on MNLI-m dataset is illustrated in Figure FIGREF1. We can see that LadaBERT outperforms other BERT-oriented model compression baselines at vari... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 5 | 3,299 | 3,883 |
16_6 |
The rest of this paper is organized as follows. First, we summarizes the related works of model compression and their applications to BERT in Section SECREF2. Then, the methodology of LadaBERT is introduced in Section SECREF3, and experimental results are presented in Section SECREF4. At last, we conclude this work a... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 6 | 3,883 | 4,452 |
16_7 | Therefore, model compression has become an indispensable technique for practice, especially in low-resource settings. In this section, we review the current progresses of model compression techniques briefly, which can be divided into four categories, namely weight pruning, matrix factorization, weight quantization an... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 7 | 4,452 | 5,132 |
16_8 | For example, Han et al. BIBREF4 proposed a method to reduce the storage and computation of neural networks by removing unimportant connections, resulting in sparse networks without affecting the model accuracy. Li et al. BIBREF5 presented an acceleration method for convolution neural network by pruning whole filters t... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 8 | 5,132 | 5,783 |
16_9 | BIBREF6 alleviated this problem by a data-driven approach that pruned zero-activation neurons iteratively based on intermediate feature maps. Zhu and Gupta BIBREF7 empirically compared large-sparse models with smaller dense models of similar parameter sizes and found that large sparse models performed better consisten... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 9 | 5,783 | 6,325 |
16_10 | BIBREF10 learned sparse neural networks through $l_0$ regularization.
Related Work ::: Matrix factorization
The goal of matrix factorization is to decompose a matrix into the product of two matrices in lower dimensions, and Singular Value Decomposition (SVD) is a popular way of matrix factorization that generalizes ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 10 | 6,325 | 6,835 |
16_11 | Matrix factorization was widely studied in the deep learning domain for model compression and acceleration BIBREF13, BIBREF14, BIBREF15. Sainath et al BIBREF13 explored a low-rank matrix factorization method of DNN layers for acoustic modeling. Xu et al. BIBREF14, BIBREF15 applied singular value decomposition to deep ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 11 | 6,835 | 7,438 |
16_12 | BIBREF17 compressed the word embedding layer via matrix factorization and achieved promising results in text classification. Winata et al. BIBREF18 carried out experiments for low-rank matrix factorization on different NLP tasks and demonstrated that it was more effective in general than weight pruning.
Related Work ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 12 | 7,438 | 8,062 |
16_13 | With weight quantization, the weights can be reduced to at most 1-bit binary value from 32-bits floating point numbers. Zhou et al. BIBREF19 showed that quantizing weights to 8-bits does not hurt the performance, and Binarized Neural Networks BIBREF20 contained binary weights and activations of only one bit. Increment... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 13 | 8,062 | 8,598 |
16_14 | Variational Network Quantization BIBREF22 formulated the problem of network quantization as a variational inference problem. Moreover, Choi et al. BIBREF23 investigated the drawbacks of conventional quantization methods based on k-means and proposed a Hessian-weighted k-means clustering algorithm as the solution.
Rel... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 14 | 8,598 | 9,271 |
16_15 | This “synthetic" label is then used to train a smaller network (the student model), which assimilates the function that is learned by the teacher model. Chen et al. BIBREF24 successfully applied knowledge distillation to object detection tasks by introducing several modifications, including a weighted cross-entropy lo... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 15 | 9,271 | 9,888 |
16_16 | BIBREF26 proposed online distillation, a variant of knowledge distillation which enabled extra parallelism for training large-scale data. In addition, knowledge distillation is also useful for aggregating model ensembles into a single model by treating the ensemble model as a teacher.
Related Work ::: Hybrid approach... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 16 | 9,888 | 10,524 |
16_17 | BIBREF28 proposed a unified framework for low-rank and sparse decomposition of weight matrices with feature map reconstructions. Polino et al. BIBREF29 advocated a combination of distillation and quantization techniques and proposed two hybrid models, i.e., quantified distillation and differentiable quantization to ad... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 17 | 10,524 | 10,963 |
16_18 | NNCF BIBREF31 provided a neural network compression framework that supported an integration of various model compression methods to generate more lightweight networks and achieved state-of-the-art performances in terms of a trade-off between accuracy and efficiency. In BIBREF32, an AutoML pipeline was adopted for mode... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 18 | 10,963 | 11,677 |
16_19 | Most existing works focus on knowledge distillation. For instance, BERT-PKD BIBREF33 is a patient knowledge distillation approach that compresses the original BERT model into a lightweight shallow network. Different from traditional knowledge distillation methods, BERT-PKD enables an exploitation of rich information i... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 19 | 11,677 | 12,185 |
16_20 | Distilled BiLSTM BIBREF34 adopts a single-layer BiLSTM as the student model and achieves comparable results with ELMo BIBREF35 through much fewer parameters and less inference time. TinyBERT BIBREF3 reports the best-ever performance on BERT model compression, which exploits a novel attention-based distillation schema ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 20 | 12,185 | 12,781 |
16_21 | Both procedures require huge resources and long training times (from several days to weeks), which is cumbersome for industrial applications. Therefore, we are aiming to explore more lightweight solutions in this paper.
Lightweight Adaptation of BERT ::: Overview
The overall pipeline of LadaBERT (Lightweight Adaptat... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 21 | 12,781 | 13,293 |
16_22 | Then, the student model is compressed towards smaller parameter size through a hybrid model compression framework in an iterative manner until the target compression ratio is reached. Concretely, in each iteration, the parameter size of student model is first reduced by $1-\Delta $ based on weight pruning and matrix f... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 22 | 13,293 | 13,978 |
16_23 | Moreover, weight pruning and matrix factorization generates better initial and intermediate status of the student model, which improve the efficiency and effectiveness of knowledge distillation. In the following subsections, we will introduce the algorithms in detail.
Lightweight Adaptation of BERT ::: Overview ::: M... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 23 | 13,978 | 14,462 |
16_24 | Without loss generality, we assume a matrix of parameters ${W} \in \mathbb {R}^{m\times n}$, the singular value decomposition of which can be written as:
where ${U} \in \mathbb {R}^{m \times p}$ and ${V} \in \mathbb {R}^{p \times n}$. | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 24 | 14,462 | 14,698 |
16_25 | ${\Sigma } =diag(\sigma _1,\sigma _2,\ldots ,\sigma _p)$ is a diagonal matrix composed of singular values and $p$ is the full rank of $W$ satisfying $p \le min(m, n)$.
To compress this weight matrix, we select a lower rank $r$. The diagonal matrix ${\Sigma }$ is truncated by selecting the top $r$ singular values. | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 25 | 14,698 | 15,014 |
16_26 | i.e., ${\Sigma }_r =diag(\sigma _1, \sigma _2,\ldots ,\sigma _r)$, while ${U}$ and ${V}$ are also truncated by selecting the top $r$ columns and rows respectively, resulting in ${U}_r \in \mathbb {R}^{m\times r}$ and ${V}_r \in \mathbb {R}^{r\times n}$. | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 26 | 15,014 | 15,268 |
16_27 |
Thus, low-rank matrix approximation of ${W}$ can be formulated as:
In this way, the original weight matrix $W$ is decomposed by the multiplication of two smaller matrices, where ${A}={U}_r\sqrt{{\Sigma }_r} \in \mathbb {R}^{n\times r}$ and ${B}={V}_r\sqrt{{\Sigma }_r} \in \mathbb {R}^{m\times r}$. These two matrices... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 27 | 15,268 | 15,654 |
16_28 |
Given a rank $r \le min(m, n)$, the compression ratio of matrix factorization is defined as:
Therefore, for a target model compression ratio $P_{svd}$, the desired rank $r$ can be calculated by:
Lightweight Adaptation of BERT ::: Overview ::: Weight pruning
Weight pruning BIBREF4 is an unstructured compression met... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 28 | 15,654 | 16,037 |
16_29 | For a neural network $f({x; \theta })$ with parameters $\theta $, weight pruning finds a binary mask ${M} \in \lbrace 0, 1\rbrace ^{|\theta |}$ subject to a given sparsity ratio, $P_{weight}$. The neural network after pruning will be $f({x; M \cdot \theta })$, where the non-zero parameter size is $||{M}||_1 = P_{weigh... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 29 | 16,037 | 16,436 |
16_30 | For example, when $P_m = 0.3$, there are 70% zeros and 30% ones in the mask ${m}$. We adopt a simple pruning strategy in our implementation: the binary mask is generated by setting the smallest weights to zeros BIBREF36.
To combine the benefits of weight pruning with matrix factorization, we leverage a hybrid approac... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 30 | 16,436 | 16,839 |
16_31 | Following Equation (DISPLAY_FORM12), SVD-based matrix factorization for any weight matrix ${W}$ can be written as: ${W}_{svd}={A}_{m\times r}{B}_{n\times r}^T$. Then, weight pruning is applied on the decomposed matrices ${A} \in \mathbb {R}^{m \times r}$ and ${B} \in \mathbb {R}^{n \times r}$ separately. | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 31 | 16,839 | 17,145 |
16_32 | The weight matrix after hybrid compression is denoted by:
where ${M_A}$ and ${M_B}$ are binary masks derived by the weight pruning algorithm with compression ratio $P_{weight}$. The compression ratio of this hybrid approach can be calculated by:
In LadaBERT, the hybrid compression produce is applied to each layer of... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 32 | 17,145 | 17,493 |
16_33 | Given an overall model compression target $P$, the following constraint should be satisfied:
where $|\theta |$ is the total number of model parameters and $P$ is the target compression ratio; $|\theta _{embd}|$ denotes the parameter number of embedding layer, which has a relative compression ratio of $P_embd$, and $|... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 33 | 17,493 | 17,941 |
16_34 | The classification layer (often MLP layer with Softmax activation) has a small parameter size ($|\theta _{cls}|$), so it is not modified in the model compression procedure. In the experiments, these fine-grained compression ratios can be optimized by random search on the validation data.
Lightweight Adaptation of BER... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 34 | 17,941 | 18,546 |
16_35 | Various types of knowledge distillation can be employed at different sub-layers. Generally, all types of knowledge distillation can be modeled as minimizing the following loss function:
Where $x$ indicates a sample input and $\mathcal {X}$ is the training dataset. $f^{(s)}({x})$ and $f^{(t)}({x})$ represent intermedi... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 35 | 18,546 | 19,074 |
16_36 | We follow the recent technique proposed by TinyBERT BIBREF3, which applies knowledge distillation constraints upon embedding, self-attention, hidden representation and prediction levels. Concretely, there are four types of knowledge distillation constraints as follows:
Embedding-layer distillation is performed upon t... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 36 | 19,074 | 19,586 |
16_37 | Mean Squared Error (MSE) is adopted as the loss function $L(\cdot )$.
Attention-layer distillation is performed upon the self-attention sub-layer. $f({x}) = \lbrace a_{ij}\rbrace \in \mathbb {R}^{n \times n}$ represents the attention output for each self-attention sub-layer, and $L(\cdot )$ denotes MSE loss function.... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 37 | 19,586 | 20,014 |
16_38 | $f({x})$ denotes the output representation of the corresponding sub-layer, and $L(\cdot )$ also adopts MSE loss function.
Prediction-layer distillation makes the student model to learns the predictions from a teacher model directly. It is identical to the vanilla form of knowledge distillation BIBREF1. It takes the s... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 38 | 20,014 | 20,493 |
16_39 |
Experiments ::: Datasets & Baselines
We compare LadaBERT with state-of-the-art model compression approaches on five public datasets of different tasks of natural language understanding, including sentiment classification (SST-2), natural language inference (MNLI-m, MNLI-mm, QNLI) and pairwise semantic equivalence (Q... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 39 | 20,493 | 21,129 |
16_40 |
Hybrid pruning is a combination of matrix factorization and weight pruning, which conducts iterative weight pruning on the basis of SVD-based matrix factorization. It is performed iteratively until the desired compression ratio is achieved.
BERT-FT, BERT-KD and BERT-PKD are reported in BIBREF33, where BERT-FT direct... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 40 | 21,129 | 21,638 |
16_41 | The student model is composed of 3 Transformer layers, resulting in a $2.5\times $ compression ratio. Each layer has the same hidden size as the pre-trained teacher, so the initial parameters of student model can be inherited from the corresponding teacher.
TinyBERT BIBREF3 instantiates a tiny student model, which ha... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 41 | 21,638 | 22,094 |
16_42 | For a fair comparison, we reproduce the TinyBERT pipeline without general distillation and data augmentation, which is time-exhaustive and resource-consuming.
BERT-SMALL has the same model architecture as TinyBERT, but is directly pre-trained by the official BERT pipeline. The performance values are inherited from BI... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 42 | 22,094 | 22,661 |
16_43 | This model requires a expensive pre-training process using the knowledge distillation constraints.
Experiments ::: Setup
We leverage the pre-trained checkpoint of base-bert-uncased as the initial model for compression, which contains 12 layers, 12 heads, 110M parameters, and 768 hidden units per layer. Hyper-paramet... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 43 | 22,661 | 23,190 |
16_44 |
For a comprehensive evaluation, we experiment with four settings of LadaBERT, namely LadaBERT-1, -2, -3 and -4, which reduce the model parameters of BERT-Base by 2.5, 5, 7.5 and 10 times respectively. In our experiment, we take the batch size as 32, learning rate as 2e-5. The optimizer is BertAdam with default settin... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 44 | 23,190 | 23,604 |
16_45 |
Experiments ::: Performance Comparison
The evaluation results of LadaBERT and state-of-the-art approaches are listed in Table TABREF40, where the models are ranked by parameter sizes for feasible comparison. As shown in the table, LadaBERT consistently outperforms the strongest baselines under similar model sizes. I... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 45 | 23,604 | 24,092 |
16_46 |
With model size of $2.5\times $ reduction, LadaBERT-1 performs significantly better than BERT-PKD, boosting the performance by relative 8.9, 8.1, 6.1, 3.8 and 5.8 percentages on MNLI-m, MNLI-mm, SST-2, QQP and QNLI datasets respectively. Recall that BERT-PKD initializes the student model by selecting 3 of 12 layers i... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 46 | 24,092 | 24,446 |
16_47 | It turns out that the discarded layers have huge impact on the model performance, which is hard to be recovered by knowledge distillation. On the other hand, LadaBERT generates the student model by iterative pruning on the pre-trained teacher. In this way, the original knowledge in the teacher model can be preserved t... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 47 | 24,446 | 24,942 |
16_48 | As shown in the results, TinyBERT does not work well without expensive data augmentation and general distillation, hindering its application to low-resource settings. The reason is that the student model of TinyBERT is distilled from scratch, so it requires much more data to mimic the teacher's behaviors. Instead, Lad... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 48 | 24,942 | 25,512 |
16_49 | Moreover, LadaBERT-3 also outperforms BERT-SMALL on most of the datasets, which is pre-trained from scratch by the official BERT pipeline on a $7.5 \times $ smaller architecture. This indicates that LadaBERT can quickly adapt to a smaller model size and achieve competitive performance without expansive re-training on ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 49 | 25,512 | 26,021 |
16_50 | Nevertheless, the performance of LadaBERT-4 is competitive on larger datasets such as MNLI and QQP. This is impressive as LadaBERT is much more efficient without exhaustive re-training on a large corpus. In addition, the inference speed of BiLSTM is usually slower than transformer-based models with similar parameter s... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 50 | 26,021 | 26,346 |
16_51 |
Experiments ::: Learning curve comparison
To further demonstrate the efficiency of LadaBERT, we visualize the learning curves on MNLI-m and QQP datasets in Figure FIGREF42 and FIGREF42, where LadaBERT-3 is compared to the strongest baseline, TinyBERT, under $7.5 \times $ compression ratio. As shown in the figures, L... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 51 | 26,346 | 26,757 |
16_52 | After training $2 \times 10^4$ steps (batches) on MNLI-m dataset, the performance of LadaBERT-3 is already comparable to TinyBERT after convergence (approximately $2 \times 10^5$ steps), achieving nearly $10 \times $ acceleration. And on QQP dataset, both performance improvement and training speed acceleration is very... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 52 | 26,757 | 27,322 |
16_53 |
Experiments ::: Effect of low-rank + sparsity
In this paper, we demonstrate that a combination of matrix factorization and weight pruning is better than single solutions for BERT-oriented model compression. Similar phenomena has been reported in the computer vision scenarios BIBREF28, which shows that low-rank and s... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 53 | 27,322 | 27,903 |
16_54 | The errors can be calculated by $\mathop {Error}=||\hat{{M}}-{M}||_1$, where $\hat{{M}}$ denotes the weight matrix after pruning.
The yellow line in Figure FIGREF44 shows the distribution of errors generated by pure weight pruning, which has a sudden drop at the pruning threshold. The orange line represents for pure ... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 54 | 27,903 | 28,414 |
16_55 | First, we apply SVD-based matrix factorization to reduce 60% of total parameters. Then, weight pruning is applied on the decomposed matrices by 50%, resulting in only 20% parameters while the error distribution changes slightly. As a result, it has smaller mean and deviation than pure matrix factorization. In addition... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 55 | 28,414 | 29,002 |
16_56 | Existing model compression methods for BERT need to be re-trained on a large corpus to reserve its original performance, which is inapplicable in low-resource settings. In this paper, we propose LadaBERT to address this problem. LadaBERT is a lightweight model compression pipeline that generates adaptive BERT model ef... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 56 | 29,002 | 29,687 |
16_57 | Therefore, LadaBERT can be easily plugged into various applications with competitive performances and little training overheads. In the future, we would like to apply LadaBERT to large-scale industrial applications, such as search relevance and query recommendation.
Figure 1: Accuracy comparison on MNLI-m dataset
Fi... | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 57 | 29,687 | 30,258 |
16_58 |
Figure 5: Distribution of pruning errors | https://arxiv.org/abs/2004.04124 | LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression | 58 | 30,258 | 30,300 |
17_0 | Neural Summarization by Extracting Sentences and Words
Traditional approaches to extractive summarization rely heavily on human-engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for single-document summarization... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 0 | 0 | 620 |
17_1 | Experimental results on two summarization datasets demonstrate that our models obtain results comparable to the state of the art without any access to linguistic annotation.
Introduction
The need to access and digest large amounts of textual data has provided strong impetus to develop automatic summarization systems... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 1 | 620 | 1,329 |
17_2 | These include surface features such as sentence position and length BIBREF0 , the words in the title, the presence of proper nouns, content features such as word frequency BIBREF1 , and event features such as action nouns BIBREF2 . Sentences are typically assigned a score indicating the strength of presence of these f... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 2 | 1,329 | 1,889 |
17_3 |
In this work we propose a data-driven approach to summarization based on neural networks and continuous sentence features. There has been a surge of interest recently in repurposing sequence transduction neural network architectures for NLP tasks such as machine translation BIBREF8 , question answering BIBREF9 , and ... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 3 | 1,889 | 2,574 |
17_4 |
We develop a general framework for single-document summarization which can be used to extract sentences or words. Our model includes a neural network-based hierarchical document reader or encoder and an attention-based content extractor. The role of the reader is to derive the meaning representation of a document bas... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 4 | 2,574 | 3,301 |
17_5 | Similar neural attention architectures have been previously used for geometry reasoning BIBREF12 , under the name Pointer Networks.
One stumbling block to applying neural network models to extractive summarization is the lack of training data, i.e., documents with sentences (and words) labeled as summary-worthy. Insp... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 5 | 3,301 | 3,962 |
17_6 | Using a number of transformation and scoring algorithms, we are able to match highlights to document content and construct two large scale training datasets, one for sentence extraction and the other for word extraction. Previous approaches have used small scale training data in the range of a few hundred examples.
O... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 6 | 3,962 | 4,659 |
17_7 | Our word-based model is similar in spirit, however, it operates over continuous representations, produces multi-sentence output, and jointly selects summary words and organizes them into sentences. A few recent studies BIBREF14 , BIBREF15 perform sentence extraction based on pre-trained sentence embeddings following a... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 7 | 4,659 | 5,369 |
17_8 | In contrast, our model summarizes documents rather than individual sentences, producing multi-sentential discourse. A major architectural difference is that our decoder selects output symbols from the document of interest rather than the entire vocabulary. This effectively helps us sidestep the difficulty of searching... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 8 | 5,369 | 5,837 |
17_9 | Gu:ea:16 and gulcehre2016pointing propose a similar “copy” mechanism in sentence compression and other tasks; their model can accommodate both generation and extraction by selecting which sub-sequences in the input sequence to copy in the output.
We evaluate our models both automatically (in terms of Rouge) and by hu... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 9 | 5,837 | 6,460 |
17_10 |
Problem Formulation
In this section we formally define the summarization tasks considered in this paper. Given a document $D$ consisting of a sequence of sentences $\lbrace s_1, \cdots , s_m\rbrace $ and a word set $\lbrace w_1, \cdots , w_n\rbrace $ , we are interested in obtaining summaries at two levels of granul... | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 10 | 6,460 | 6,924 |
17_11 | We do this by scoring each sentence within $D$ and predicting a label $y_L \in {\lbrace 0,1\rbrace }$ indicating whether the sentence should be included in the summary. | https://arxiv.org/abs/1603.07252 | Neural Summarization by Extracting Sentences and Words | 11 | 6,924 | 7,093 |
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