2020 International Joint Conference on Neural Networks (IJCNN). In the probability model framework, a variational autoencoder contains a specific probability model of data x x and latent variables z z. Title: Conditional Variational Autoencoder for Neural Machine Translation et al., we augment the encoder-decoder NMT paradigm by introducing a continuous Gregor, Karol, Danihelka, Ivo, Graves, Alex, Rezende, DaniloJimenez, and Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. Mach Learn 110(8):21872211, CrossRef xcbdg`b`8 $8AL+H0L@$ Conditional Variational Autoencoder for Neural Machine Translation coder RNN similarly predicts the next target word y iusing the previously generated output and its own hidden state. Autoencoder - Wikipedia 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. In this section, we introduce Non-deterministic and Emotional Chatting Machine (NECM), which aims to generate colloquial responses that vary with the same input and emotional context (i.e., Angry, Disgust, Happy, Like, Sad or Other).We build our framework based on conditional variational autoencoders (CVAE) that captured and learnt the emotive characteristics by latent variables z. 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Kamalov, F., Ali-Gombe, A., Moussa, S. (2023). Conditional Variational Autoencoder for Neural Machine Translation To verify that the latent space is smooth, we interpolate across the latent space and observe sentences generated. Typically, the latent space z produced by the encoder is sparsely populated, meaning that it is difficult to predict the distribution of values in that . Variational Autoencoder: Introduction and Example 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). Concretely, the proposed method utilizes a conditional variational autoencoder (VAE) to learn the latent variables underpinning the distribution of minority labels. Firuz Kamalov . 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. Let's get into the code. Introduction to variational autoencoders (VAE) - The Learning Machine Expert Syst Appl 175:114750, Lin WC, Tsai CF, Hu YH, Jhang JS (2017) Clustering-based undersampling in class-imbalanced data. ICT Analysis and Applications pp 661669Cite as, Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 517). More than a million books are available now via BitTorrent. << /Names 300 0 R /OpenAction 60 0 R /PageMode /UseNone /Pages 287 0 R /Type /Catalog >> 48 0 obj ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). is a multivariate Gaussian with mean and variance matrices parametrized by neural networks. in the inference x[Qo~c9CveP2ENVv"Z(lnwyok .8%SvTp]du\T:U5;;Q:] 9O\#kqJAW9q,XcEc collapse. However, as expected with VAEs for text, we ran into the challenge of posterior collapse for our standard CVAE model. 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. Finally, we. Abstract: We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). A VAE can generate samples by first sampling from the latent space. We use the same network architecture proposed in VNMT (Zhang etal., 2016). In experiment 1, we show that the addition of our co-attention mechanism significantly improves the expressiveness of the approximate posterior network. Variational Recurrent Neural Machine Translation | Proceedings of the Inf Sci 569:508526, Zhang W, Li X, Jia XD, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Conditional Variational Autoencoder for Neural Machine Translation - CORE Latent Visual Cues for Neural Machine Translation, A Discriminative Latent-Variable Model for Bilingual Lexicon Induction, Latent Alignment and Variational Attention, Mixture Models for Diverse Machine Translation: Tricks of the Trade, Kanerva++: extending The Kanerva Machine with differentiable, locally The attention mechanism introduced in (Bahdanau etal., 2014) enhances this model by aligning source and target words using the encoder RNN hidden states. Appl Soft Comput 83:105662, Li Z, Huang M, Liu G, Jiang C (2021) A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection. 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. generation in the context of neural machine translation (NMT). It reports improvements over vanilla neural machine translation baselines on Chinese-English and English-German tasks. To explore the latent space learned by the model, we sample and generate multiple sequences. However, despite much work, these two problems have not been solved. IEEE, pp 14, Kamalov F, Thabtah F, Leung HH (2022) Feature selection in imbalanced data. Conditional Variational Autoencoder Networks for Autonomous Vehicle Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. It is one of the most popular generative models which generates objects similar to but not identical to a given dataset. Using Variational Autoencoder (VAE) to Generate New Images Conditional variational autoencoder (CVAE) We selected the CVAE as a molecular generator. "Improved variational autoencoders for text modeling using dilated convolutions." arXiv preprint arXiv:1702.08139 (2017). CVAEs, form of directed, graphical models, are exploited to model the probability distribution of highband features conditioned on narrowband features and their combination with adversarial learning give further improvements. 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. autoencoder non image data The generated samples are quite diverse, mentioning topics such as shuffling, beds, colonization, discrimination, etc. There are many ways in which a sentence can be translated due to the multimodal nature of natural language, and latent variable models aim at capturing precisely these translation specificities through the latent variable. Variational autoencoder - Wikipedia Variational Recurrent Neural Machine Translation Inf Sci 512:11921201, CrossRef Download Citation | Conditional Variational Autoencoder-Based Sampling | Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias . We explore the performance of latent variable models for conditional text 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. Neural machine translation with phrase-level universal visual representations. The subject of this article is Variational Autoencoders (VAE). Results are shown below in Table 2. 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. arXiv preprint . Compared to the vision domain, latent variable models for text face Similar to Zhang 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. Figure 1 shows 20 sampled sentences for each example, ranked by log probability. 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. endobj 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. latent variable without weakening the translation model. We use the IWSLT 2016 German-English dataset for our experiments, consisting of 196k sentence pairs. First, we obtain a fixed dimensional representation of the sentence by mean-pooling the annotation vectors produced by the neural encoder over the source sentence. The posterior is still updated through the reconstruction error term, but the prior is not updated, as it only appears in the KL term. However, inference in these models can often be difficult or intractable, motivating a class of variational methods that frame the inference problem as optimization. In the same spirit as the co-attention technique described in (Parikh etal., 2016), we compute pairwise dot attention coefficients between the words of the source sentence and each word of the target sentence, and vice versa. Sutskever, Ilya, Vinyals, Oriol, and Le, QuocV. Sequence to sequence learning with neural networks. << /Linearized 1 /L 714204 /H [ 4842 253 ] /O 51 /E 79666 /N 9 /T 713659 >> Similar to Zhang https://doi.org/10.1007/978-981-19-5224-1_66, DOI: https://doi.org/10.1007/978-981-19-5224-1_66, eBook Packages: EngineeringEngineering (R0). Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially. Improved Variational Neural Machine Translation by Promoting Mutual Our main results comparing discriminative attention-based translation with a few of our CVAE models are shown in Table1. In experiment 3, we show an exploration of the latent space. We explored concatenating a self-attention context vector to the mean-pool of the annotation vectors. We compared three models: vanilla sequence-to-sequence with dot-product attention, VNMT (Zhang etal., 2016), and our Conditional VAE with co-attention. Then we add a linear projection layer. Proceedings of the 2016 Conference on Empirical Methods in 46 0 obj Google Scholar, Bagui S, Li K (2021) Resampling imbalanced data for network intrusion detection datasets. . poem sequentially using an improved conditional variational autoencoder. What is a Variational Autoencoder (VAE)? Modeling coverage for neural machine translation. Conditional Variational Autoencoder Networks for Autonomous Vehicle Letting x denote the conditioning/input variable, y the output variable, and z the latent variable, a CVAE consists of three components: the prior p ( z x), which generates the latent variable z using only the input x and the . Once trained, for an observation of an agent's motion \(O_{EV}\), we acquire a sample from the stochastic latent space and condition the decoder on vector c to produce agent predictions.. CVAE-I. Finally, we demonstrate some exploration of the learned latent space in our conditional variational model. Conditional Variational Autoencoder for Neural Machine Translation - DeepAI 3a. We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). 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. It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. latent variable to model features of the translation process. Emotional Dialogue Generation Based on Conditional Variational 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. Archive Torrent Books : Free Audio : Free Download, Borrow and We extend this model with a co-attention mechanism motivated by Parikh et al. Inf Sci 409:1726, Moniz N, Monteiro H (2021) No free lunch in imbalanced learning. We propose a conditional variational model for machine translation, extending the framework introduced by (Zhang etal., 2016) with a co-attention based inference network and show improvements over discriminitive sequence-to-sequence translation and previous variational baselines. (eds) ICT Analysis and Applications. CVAEs, as introduced in Sohn, et al (2015), make no assumptions on the conditioning variable. 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