Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. Inf Sci 512:11921201, CrossRef We can write the joint probability of the model as p (x, z) = p (x \mid z) p (z) p(x,z) = p(x z)p(z). IEEE Access 9:129088129101, Kamalov F, Denisov D (2020) Gamma distribution-based sampling for imbalanced data. We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). We use Bahdanaus attention decoder (Bahdanau etal., 2014) with the incorporation of the dependence on the latent variable z. We extend this model with a co-attention mechanism motivated by Parikh et al. In experiment 3, we show an exploration of the latent space. MATH The present paper proposes an autoencoder-based sampling approach to balance the data. Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. MATH Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Neurocomputing 459:481493, Thabtah F, Hammoud S, Kamalov F, Gonsalves A (2020) Data imbalance in classification: experimental evaluation. https://doi.org/10.1007/978-981-19-5224-1_66, Shipping restrictions may apply, check to see if you are impacted, Tax calculation will be finalised during checkout. This has been accomplished by combining the two types of data. Both prior and posterior distributions are assumed to be multivariate Gaussians. We compared three models: vanilla sequence-to-sequence with dot-product attention, VNMT (Zhang etal., 2016), and our Conditional VAE with co-attention. . The vector z is concatenated before the last projection layer to the context vector and the LSTM hidden state. Bowman, SamuelR., Vilnis, Luke, Vinyals, Oriol, Dai, AndrewM., In the unconditional setting, minimum KL budgeting can be achieved through the use of von Mises Fisher distribution, with uniform prior. al. Kim, Yoon, Wiseman, Sam, Miller, AndrewC., Sontag, David, and Rush, These experiments are done with the CVAE model trained with KL coefficient of 0.25. We finally project to the mean vector and the scale vector: is the identity matrix. Our model extends the conditional variational autoencoder (CVAE) with two new ingredients: first, we propose . % network. However, inference in these models can often be difficult or intractable, motivating a class of variational methods that frame the inference problem as optimization. We explored with setting a minimum for the KL, adding a coefficient to the KL penalty term in the ELBO, and word dropout. arXiv preprint arXiv:1312.6114, Kovcs G (2019) An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Similar to Zhang In: 2021 International symposium on networks, computers and communications (ISNCC). It reports improvements over vanilla neural machine translation baselines on Chinese-English and English-German tasks. Concretely, the proposed method utilizes a conditional variational autoencoder (VAE) to learn the latent variables underpinning the distribution of minority labels. Similar work has been done in[Wanget al., 2016b], the main distinction from our work to theirs is the implemented neural model is the conditional variational au-toencoders in our work. "FC" denotes a fully-connected layer. #rRp*j5OQ?AT9F#-b!S]k'O!G@5?7_fzql\(tAYy+hER@dMq&. The hidden state hj is produced at each step by a LSTM that takes as input z and the word embedding of word yj. model with a co-attention mechanism motivated by Parikh et al. Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. Introduction. show that our conditional variational model improves upon both discriminative in the inference This is a preview of subscription content, access via your institution. The present paper proposes an autoencoder-based sampling approach to balance the data. A Conditioned Variational Autoencoder for constrained top-N item recommendation where the recommended items must satisfy a given condition is proposed, and it is suggested that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation. where y is the network output, \(z^{(l)}\) is a Gaussian latent variable, and L is the number of samples. latent variable to model features of the translation process. Knowl Based Syst 207:106368, Kamalov F (2020) Kernel density estimation based sampling for imbalanced class distribution. Latent variables are sampled from the approximate posterior during training, but from the prior during generation. We explore the performance of latent variable models for conditional text Edit social preview. Hence, the reconstruction error here is a measure of the ability of the approximate posterior to encode information relevant to reconstructing the target sequence. We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). KL cost annealing and 2. masking parts of the source and target tokens with symbols in order to strengthen the inferer by weakening the decoder (word dropouts). Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable. The variational autoencoder (VAE) (Kingma & Welling, 2013) is a generative model that uses deep neural nets to predict parameters of the variational distribution. By setting KL coefficient to 0.25 (described above), we are able to train a model that utilizes the latent variable model much more, and still outperform sequence-to-sequence in terms of BLEU. In the first example, the source sentence contains several tokens and thus there is a lot of uncertainty to what the sentence could mean. Models of neural machine translation are of-ten from a discriminative family of encoder-decoders that learn a conditional distribution of a target sentence given a source sentence. Expert Syst Appl 91:464471, Gan D, Shen J, An B, Xu M, Liu N (2020) Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis. standard neural machine translation) in all such scenarios. Compared to the vision domain, latent variable models for text face It assumes that the data is generated by some random process, involving an unobserved continuous random variable z. it is assumed that the z is generated from some prior distribution P_ (z) and the data is generated from some condition distribution P_ (X|Z), where X represents that data. Autoencoders are an unsupervised learning model that aim to learn distributed representations of data.. Gregor, Karol, Danihelka, Ivo, Graves, Alex, Rezende, DaniloJimenez, and collapse. The generated samples are quite diverse, mentioning topics such as shuffling, beds, colonization, discrimination, etc. We also present our experiments testing various methods of addressing common challenges of applying VAEs to text (Bowman etal., 2015), namely posterior collapse. Where Ex, and Ey are learned source and target word embeddings. From these explorations, we confirm that the model is learning a meaningful and smooth latent space that can guide the translation process. our model extends the conditional variational autoencoder (cvae) with two new ingredients: first, we propose a modified evidence lower bound (elbo) objective which explicitly promotes mutual information; second, we regularize the probabilities of the decoder by mixing an auxiliary factorized distribution which is directly predicted by the latent In the standard Recurrent Neural Net (RNN)-based encoder-decoder setting, the encoder RNN represents the source sentence by learning sequentially from the previous source word xi and an evolving hidden state, while the decoder RNN similarly predicts the next target word yi using the previously generated output and its own hidden state. These examples illustrate some of the semantic and stylistic attributes of the translation process that can be captured by the latent variable. More than a million books are available now via BitTorrent. In this paper, we propose a variational model to learn this conditional distribution for neu-ral machine translation: a variational encoder- This demonstrates that latent variables could encode diverse semantic information. Proceedings of the 32nd International Conference on Machine We demonstrate the effectiveness of our model in three machine translation scenarios: in-domain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data. block allocated latent memory, Latent Part-of-Speech Sequences for Neural Machine Translation, Target Conditioning for One-to-Many Generation, http://jmlr.org/proceedings/papers/v37/gregor15.html, https://doi.org/10.1162/neco.1997.9.8.1735, http://aclweb.org/anthology/D/D16/D16-1244.pdf, http://jmlr.org/proceedings/papers/v32/rezende14.html, http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks, http://aclweb.org/anthology/P/P16/P16-1008.pdf, http://aclweb.org/anthology/D/D16/D16-1050.pdf. Although. Google Scholar, Shamsolmoali P, Zareapoor M, Shen L, Sadka AH, Yang J (2021) Imbalanced data learning by minority class augmentation using capsule adversarial networks. << /Linearized 1 /L 714204 /H [ 4842 253 ] /O 51 /E 79666 /N 9 /T 713659 >> Finally, we demonstrate some exploration of the learned latent space in our conditional variational model. Although word embeddings are already continuous representations of words, the additional LSTM step introduces contextual information that is unique to the sentence. It analyzes challenges for training variational models for text (primarily posterior collapse) and propose two workarounds: 1. We found that the posterior used in VNMT (Zhang etal., 2016), which simply takes the concatenated mean-pool vectors of the source and target codes, does not capture interactions between the source and the target sentences. Intuitively, having access to both sentences introduces the possibility of finding important stylistic translation decisions by comparing the two sentences. We use the same network architecture proposed in VNMT (Zhang etal., 2016). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. In experiment 2, we present a comparison between various methods of combating posterior collapse. Extending word dropout as used in (Bowman etal., 2015), we weaken the encoder-decoder portion of the model to steer the model to make greater use of the latent variable when translating. All models used 300 dimensional word embeddings, 2 layer encoder and decoder LSTMs with hidden dimensions of size 300. Note: for BLEU score calculation in our current results, we retain the unk tokens and thus may not be directly comparable to other published results. The structure of variational multimodal machine translation (VMMT) based on a LSTM network with an attention mechanism; (b). The posterior is still updated through the reconstruction error term, but the prior is not updated, as it only appears in the KL term. endstream Now, let's get our hands dirty with some code. CVAE seeks to maximize logp(y|x), and the variational objective becomes: Here, CVAE can be used to guide NMT by capturing features of the translation process into the latent variable z. We experiment with a conditional latent variable model applied to the task of translation. 46 0 obj Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. We propose a conditional variational model for machine translation, extending the framework introduced by (Zhang et al., 2016) with a co-attention based inference network and show improvements over discriminitive sequence-to-sequence translation and previous variational baselines. In this lecture Tensor Flow Implementation of Conditional Variational Auto Encoder is discussed#autoencoder#variational#colab This work uses a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer and proves its effectiveness not only in Transformers but also in the most relevant encoder-decoder based LM, seq2seq with and without attention. Each component of the model is conditioned on some observed x, and models the generation process according to the graphical model shown below. {Hh8 K,x4H/(1EQv i Fray Vicente Solano 4-31 y Florencia Astudillo An Autoencoder can be also useful for dimensionality reduction and denoising images, but can also be successful in unsupervised machine translation. Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper. A variational autoencoder (VAE) is a type of neural network that learns to reproduce its input, and also map data to latent space. Additionally, attention mechanisms. From sampling and generating (Figure 1), we observe that the model is able to produce somewhat diverse sentences. generative models. The model that we propose relies on an encoder-decoder translation architecture similar to (Bahdanau etal., 2014) along with an inferer network. this work extends the conditional variational autoencoder with two new ingredients: first, it proposes a modified evidence lower bound (elbo) objective which explicitly promotes mutual information; second, it regularize the probabilities of the decoder by mixing an auxiliary factorized distribution which is directly predicted by the latent Comput Ind Eng 140:106266, Grekow J, Dimitrova-Grekow T (2021) Monophonic music generation with a given emotion using conditional variational autoencoder. et al., we augment the encoder-decoder NMT paradigm by introducing a continuous Artidoro Pagnoni, Kevin Liu, Shangyan Li. << /Names 300 0 R /OpenAction 60 0 R /PageMode /UseNone /Pages 287 0 R /Type /Catalog >> Similar to Zhang et al., we augment the. +91-33-40048937 / +91-33-24653767 (24x7) /+91 8584039946 /+91 9433037020 / +91 9748321111 ; horse's slow gait - crossword We also included it as a skip connection in the LSTM input by concatenating it to the word embedding of the target words at each time step. A special long short-term memory (LSTM) architecture for NLI that remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label and achieves an accuracy of 86.1%, outperforming the state of the art. We extend this We extend their model with a co-attention mechanism, motivated by (Parikh etal., 2016), in the inference network and show that this change leads to a more expressive approximate posterior. 2 Highly Influenced PDF View 3 excerpts, cites methods The decoder models the probability of a target sentence. generation in the context of neural machine translation (NMT). A novel Grounded Visual Description Conditional Variational Autoencoder (GVD-CVAE) is proposed and introduced, which introduces a discrete random variable that models each word-to-region alignment, and learns its approximate posterior distribution given the full sentence. 48 0 obj 2022 International Joint Conference on Neural Networks (IJCNN). To assess the contribution of our co-attention based approximate posterior, we compare the reconstruction losses of our model and the VNMT model (Zhang etal., 2016). We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). (Kim etal., 2018) is one of the first LSTM generative models to outperform language models by using a latent code. 3a. Jzefowicz, Rafal, and Bengio, Samy. The context vector cj is the result of a convex combination of the annotation vectors hx produced by the encoder applied to the source sentence x. Yang, Zichao, et al. Parikh, AnkurP., Tckstrm, Oscar, Das, Dipanjan, and Uszkoreit, latent variable without weakening the translation model. Our model extends the conditional variational autoencoder . This work introduces and study an RNN-based variational autoencoder generative model that incorporates distributed latent representations of entire sentences that allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. The results of numerical experiments reveal the potency of the suggested technique on several datasets. This will be projected to the mean vector and variance matrix just like in the prior network: Through the use of the co-attention network, the mean and variance parameters of the posterior capture interactions between source and target sentences. Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, with the KL term of the ELBO objective zeroed out. Furthermore, this result also confirms that capturing interactions between source and target sentences through co-attention helps provide effective information to the latent variable about the specificities of the translation process. Our variational models used Monte Carlo sampling and the reparameterization trick for gradient estimation (Kingma & Welling, 2013; Rezende etal., 2014). endobj We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. As expected, there is a trade-off between reconstruction error and KL. Stochastic backpropagation and approximate inference in deep Conditional variational autoencoder (CVAE) is an extension of VAE to conditional tasks such as translation. Then, the trained encoder is employed to produce new minority samples to equalize the sample distribution. Google Scholar, Bagui S, Li K (2021) Resampling imbalanced data for network intrusion detection datasets. autoencoder non image data chopin fantasie in f minor imslp autoencoder non image data nba youngboy new album the last slimeto autoencoder non image data. As determined by the ELBO equation, the parameters of the prior are computed by the prior network which only takes the source sentence as input. 9qKF$E\SqJvHp"jp K)q53VW1W*3ET3>IE: 6I-L/$*"Btb%'7(Nv]2~E7j D`(X[Cfa/H&&|(bF.cUU4$`OAa (0(Qjcn$H>l$F c2hhqx We show that our conditional variational model improves upon both discriminative attention-based translation and the variational baseline presented in (Zhang et al., 2016). Machine translation is a classic, conditional language modeling task in NLP, and was one of the first in which deep learning techniques trained end-to-end have been shown to outperform classical phrase-based pipelines. VMMT (a) takes both text x 1 = s 1, s 2, s 3, and images x 2 as input. 2022 Springer Nature Switzerland AG. translate. We extend this The generative process can be written as follows. Neural machine translation by jointly learning to align and We also present and compare various ways of mitigating the problem of posterior collapse that has plagued latent variable models for text. Hochreiter, Sepp and Schmidhuber, Jrgen. The autoencoder learns a representation for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data. Then we add a linear projection layer. << /Type /ObjStm /Length 2310 /Filter /FlateDecode /N 95 /First 824 >> We explore variational autoencoder networks to get multimodal predictions of agents. example shows variation in wording: In the middle of the 1990s, center of the 1990s, In the 1990s, etc. The neural inferer can be divided in two parts: the prior and the posterior networks. A VAE can generate samples by first sampling from the latent space. illustrate what the latent variable is capable of capturing. This section discusses recent efforts in neural machine translation, variational autoencoders (VAE), and their extension to the conditional case (CVAE). Tu, Zhaopeng, Lu, Zhengdong, Liu, Yang, Liu, Xiaohua, and Li, Hang. (eds) ICT Analysis and Applications. We trained each of our models end-to-end with Adam (Kingma & Ba, 2014) with initial learning rate 0.002, decayed by a scheduler on plateau. attention-based translation and the variational baseline presented in Zhang et For Variational Recurrent NMT, I tested only using the current RNN state is sufficient to achieve good performance. (Bowman etal., 2015) presents a basic RNN-based VAE generative model to explicitly model holistic properties of sentences. Thus, given the distribution, we can sample a random noise and produce . The principal issue with setting an explicit minimum to the KL term is that when the KL term is smaller than the predefined value, there is no gradient propagated through the KL objective. Firuz Kamalov . Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. For our variational models, we use a KL warm-up schedule by training a modified objective: for the first five training epochs, then annealed linearly over the next ten epochs. network. We use a bidirectional LSTM (Hochreiter & Schmidhuber, 1997), to produce annotation vectors for words in both the source sentence. Mixture models trained via EM are among the simplest, most widely used a Episodic and semantic memory are critical components of the human memory Learning target side syntactic structure has been shown to improve Neura Neural Machine Translation (NMT) models often lack diversity in their Yes. Jagadish DN, Chauhan A, Mahto L (2021) Autonomous vehicle path prediction using conditional variational autoencoder networks . We latent variable without weakening the translation model. This paper introduces MDSumma, which masks partial tokens in decoder, aiming to alleviate the over-reliance on the antecedent, and employs a variational autoencoder model, sampling continuous latent variables from the probability distribution to explicitly model underlying semantics of the target summaries. We restrict the variance matrices of the prior and the posterior distributions to be diagonal. 1 Answer. Part of Springer Nature. This work proposes a Semantic Regularized class-conditional Generative Adversarial Network, which is referred to as SReGAN, and incorporates an additional discriminator and classifier into the generator-discriminator minimax game. ]-W=bb/WDd,mLFL\yL[Xc> |dDID7acsgKs^-tNZc-QirUoK2HAx0m@b3MA8y!Qz85`Leob D_!J:\Zv`2c6v4$dQr'-X7G(=|S%>/|8nHa]@U;JHHn"\$2AMPK IS1yF*PdupB{foLU'^D@RbBEnzKDuVz(fB04-DEH:$3#Dfo_,iUgBc{\B2ZhYSV`ypNri\\9p%*+Yzq9Jk}~]nnWE]@Efc&vaS|ndt-^=;g!~Qy2r]-iUR+E_F/Uyaat/H;cGOEGg!,b1Hbd\fe3d'Ge b)f@TYLC]E6audF Finally, we add a linear projection layer and a non linearity, and get the final fixed dimensional vector. What is a Variational Autoencoder (VAE)? show that our conditional variational model improves upon both discriminative The variational autoencoder or VAE is a directed graphical generative model which has obtained excellent results and is among the state of the art approaches to generative modeling. Current NMT models generally use the encoder-decoder framework, where an encoder transforms a source sequence to a distributed representation, which the decoder then uses to generate the target sequence. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ACM Comput Surv (CSUR) 52(4):136, Kingma DP, Welling M (2013) Auto-encoding variational bayes. Conditional Variational Autoencoder-Based Sampling. Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. +593 7 2818651 +593 98 790 7377; Av. In machine learning, a variational autoencoder (VAE), is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling, belonging to the families of probabilistic graphical models and variational Bayesian methods.. Variational autoencoders are often associated with the autoencoder model because of its architectural affinity, but with significant differences in the . This paper proposes a generative XAI framework, INTERACTION (explain aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder), which achieves competitive or better performance against state-of-the-art baseline models on explanation generation and prediction. Abstract We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Different from the vanilla . Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Here, all gradients will only be backpropagated through the reconstruction error, eliminating the KL regularization of the approximate posterior to resemble the prior. 2020 International Joint Conference on Neural Networks (IJCNN). Springer, Singapore. A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. We explored concatenating a self-attention context vector to the mean-pool of the annotation vectors. latent variable to model features of the translation process. We found that this approach lead to a more representative posterior network, which gave better results. (Zhang etal., 2016) introduce a framework and baseline for conditional variational models and apply it to machine translation. Ann Data Sci 115, Kaur H, Pannu HS, Malhi AK (2019) A systematic review on imbalanced data challenges in machine learning: applications and solutions. In the context of variational autoencoders, it is crucial that the posterior network is as expressive as possible. It proposes a hybrid approach between amortized variational inference (AVI) to initialize variational parameters and stochastic variational inference (SVI) to iteratively refine them . The probabilistic decoder model seeks to maximize p(y|x), the likelihood of output sequence y given source input x. Solid lines denote the generation process and dashed lines denote the variational approximation. We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. << /Type /XRef /Length 87 /Filter /FlateDecode /DecodeParms << /Columns 4 /Predictor 12 >> /W [ 1 2 1 ] /Index [ 46 255 ] /Info 44 0 R /Root 48 0 R /Size 301 /Prev 713660 /ID [<75023e9100a39a605ac6ea836ad5c7a5>] >>
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