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In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs). We investigate the impact of weak supervision by creating a weakly supervised dataset and show that even a slight amount of supervision can significantly improve the model performance and enable better-quality transfers. We examine different model architectures, and evaluation metrics, proposing a novel Commonsense KB completion metric tailored for generative models. Extensive experimental results show that the proposed method compares very favorably to the existing baselines. This approach is a viable step towards a more advanced system for automatic KB construction/expansion and the reverse operation of KB conversion to coherent textual descriptions., Comment: Equal Contributions of Authors Pierre L. Dognin, Igor Melnyk, and Inkit Padhi. Accepted at EMNLP'20

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer., Comment: ACL 2020

Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock their potential. Here we propose neural network models that represent tabular time series that can optionally leverage their hierarchical structure. This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences. We demonstrate our models on two datasets: a synthetic credit card transaction dataset, where the learned representations are used for fraud detection and synthetic data generation, and on a real pollution dataset, where the learned encodings are used to predict atmospheric pollutant concentrations. Code and data are available at https://github.com/IBM/TabFormer., Comment: Accepted to ICASSP, 2021; https://github.com/IBM/TabFormer

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution. However, the efficacy of PFs as key source of information for learning generative models is not well studied. We investigate here the use of PFs in the context of learning implicit generative models through moment matching (MM). More specifically, we propose a new effective MM approach that learns implicit generative models by performing mean and covariance matching of features extracted from pretrained ConvNets. Our proposed approach improves upon existing MM methods by: (1) breaking away from the problematic min/max game of adversarial learning; (2) avoiding online learning of kernel functions; and (3) being efficient with respect to both number of used moments and required minibatch size. Our experimental results demonstrate that, due to the expressiveness of PFs from pretrained deep ConvNets, our method achieves state-of-the-art results for challenging benchmarks., Comment: 16 pages

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training., Authors Equal Contribution, CVPR 2019

This paper proposes an acoustic modeling approach based on bootstrap and restructuring to dealing with data sparsity for low-resourced languages. The goal of the approach is to improve the statistical reliability of acoustic modeling for automatic speech recognition (ASR) in the context of speed, memory and response latency requirements for real-world applications. In this approach, randomized hidden Markov models (HMMs) estimated from the bootstrapped training data are aggregated for reliable sequence prediction. The aggregation leads to an HMM with superior prediction capability at cost of a substantially larger size. For practical usage the aggregated HMM is restructured by Gaussian clustering followed by model refinement. The restructuring aims at reducing the aggregated HMM to a desirable model size while maintaining its performance close to the original aggregated HMM. To that end, various Gaussian clustering criteria and model refinement algorithms have been investigated in the full covariance model space before the conversion to the diagonal covariance model space in the last stage of the restructuring. Large vocabulary continuous speech recognition (LVCSR) experiments on Pashto and Dari have shown that acoustic models obtained by the proposed approach can yield superior performance over the conventional training procedure with almost the same run-time memory consumption and decoding speed.

Deep Scattering Network features introduced for image processing have recently proved useful in speech recognition as an alternative to log-mel features for Deep Neural Network (DNN) acoustic models. Scattering features use wavelet decomposition directly producing log-frequency spectrograms which are robust to local time warping and provide additional information within higher order coefficients. This paper extends previous works by showing how scattering features perform on a state-of-the-art spontaneous speech recognition utilizing DNN acoustic model. We revisit feature normalization and compression topics in an extensive study, putting emphasis on comparing models of the same size. We observe that scattering features outperform baseline log-mel in all conditions, with additional gains from multi-resolution processing.

A significant barrier to progress in automatic speech recognition (ASR) capability is the empirical reality that techniques rarely “scale”—the yield of many apparently fruitful techniques rapidly diminishes to zero as the training criterion or decoder is strengthened, or the size of the training set is increased. Recently we showed that annealed dropout—a regularization procedure which gradually reduces the percentage of neurons that are randomly zeroed out during DNN training—leads to substantial word error rate reductions in the case of small to moderate training data amounts, and acoustic models trained based on the cross-entropy (CE) criterion [1]. In this paper we show that deep Maxout networks trained using annealed dropout can substantially improve the quality of commercial-grade LVCSR systems even when the acoustic model is trained with sequence-level training criterion, and on large amounts of data.

In this paper, we present new methods for parameterizing the connections of neural networks using sums of direct products. We show that low rank parameterizations of weight matrices are a subset of this set, and explore the theoretical and practical benefits of representing weight matrices using sums of Kronecker products. ASR results on a 50 hr subset of the English Broadcast News corpus indicate that the approach is promising. In particular, we show that a factorial network with more than 150 times less parameters in its bottom layer than its standard unconstrained counterpart suffers minimal WER degradation, and that by using sums of Kronecker products, we can close the gap in WER performance while maintaining very significant parameter savings. In addition, direct product DBNs consistently outperform standard DBNs with the same number of parameters. These results have important implications for research on deep belief networks (DBNs). They imply that we should be able to train neural networks with thousands of neurons and minimal restrictions much more rapidly than is currently possible, and that by using sums of direct products, it will be possible to train neural networks with literally millions of neurons tractably-an exciting prospect.

We present the Factorial Hidden Restricted Boltzmann Machine (FHRBM) for robust speech recognition. Speech and noise are modeled as independent RBMs, and the interaction between them is explicitly modeled to capture how speech and noise combine to generate observed noisy speech features. In contrast with RBMs, where the bottom layer of random variables is observed, inference in the FHRBM is intractable, scaling exponentially with the number of hidden units. We introduce variational algorithms for efficient approximate inference that scale linearly with the number of hidden units. Compared to traditional factorial models of noisy speech, which are based on GMMs, the FHRBM has the advantage that the representations of both speech and noise are highly distributed, allowing the model to learn a parts-based representation of noisy speech data that can generalize better to previously unseen noise compositions. Preliminary results suggest that the approach is promising.

In this paper we describe how the model-based noise robustness algorithm for previously unseen noise conditions, Dynamic Noise Adaptation (DNA), can be made robust to matched data, without the need to do any system re-training. The approach is to do online model selection and averaging between two DNA models of noise: one that is tracking the evolving state of the background noise, and one clamped to the null mis-match hypothesis. The approach, which we call DNA with (matched) condition detection (DNA-CD), improves the performance of a commerical-grade speech recognizer that utilizes feature-space Maximum Mutual Information (fMMI), boosted MMI (bMMI), and feature-space Maximum Likelihood Linear Regression (fMLLR) compensation by 15% relative at signal-to-noise ratios (SNRs) below 10 dB, and over 8% relative overall.

In model-based pattern recognition it is often useful to change the structure, or refactor, a model. For example, we may wish to find a Gaussian mixture model (GMM) with fewer components that best approximates a reference model. One application for this arises in speech recognition, where a variety of model size requirements exists for different platforms. Since the target size may not be known a priori, one strategy is to train a complex model and subsequently derive models of lower complexity. We present methods for reducing model size without training data, following two strategies: GMM-approximation and Gaussian clustering based on divergences. A variational expectation-maximization algorithm is derived that unifies these two approaches. The resulting algorithms reduce the model size by 50% with less than 4% increase in error rate relative to the same-sized model trained on data. In fact, for up to 35% reduction in size, the algorithms can improve accuracy relative to baseline.

This paper focuses on the optimization of model parameters for vocal tract length normalization (VTLN). For maximum likelihood (ML) based normalization techniques, the complexity of the VTL-models is a source of variation in system performance. An optimal complexity for the VTL-model that ensures best global word error rate is proposed. The choice of frequency warping factor also depends on the signal processing step of VTLN. A best set of parameters for the VTLN signal processing stage is proposed with extensive results for an optimal frequency range.

This paper describes the BYBLOS system that BBN used to participate in the 2001 NIST Hub-5 evaluation benchmark. We outline the procedure used for training and decoding, and present the algorithmic improvements made to the system, along with experimental results. These improvements include a Gaussian splitting initialization procedure, the use of Linear Discriminant Analysis, and processing of additional acoustic training data. We also discuss our system combination and confidence-based thresholding methods. Experiments on an internal validation test set show that all these system improvements provide a 8.1 % relative reduction in word error rate compared to our 2000 LVCSR system.