This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small changes in the input such as non-canonical input (e.g., typos). Yet both stability and robustness are desired properties in applications involving user-generated content, and the more as humans easily cope with such noisy or adversary conditions. In this paper, we study the impact of noisy input. We consider different noise distributions (one type of noise, combination of noise types) and mismatched noise distributions for training and testing. Moreover, we empirically evaluate the robustness of different models (convolutional neural networks, recurrent neural networks, non-neural models), different basic units (characters, byte pair encoding units), and different NLP tasks (morphological tagging, machine translation).
A large body of work on regularization techniques to learn robust representations and models exists. Examples include @math -regularization, dropout @cite_4 , Jacobian-based sensitivity penalty @cite_2 @cite_7 , and data noising. Compared to other application domains such as vision @cite_18 @cite_21 and speech @cite_5 @cite_14 @cite_27 @cite_9 , working on noisy data @cite_16 @cite_26 @cite_24 and in particular data noising @cite_22 , do not have a long and extensive history in NLP.
@cite_18 @cite_14 @cite_4 @cite_26 @cite_7 @cite_22 @cite_9 @cite_21 @cite_24 @cite_27 @cite_2 @cite_5 @cite_16
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This paper presents the progress of acoustic models for lowresourced languages (Assamese, Bengali, Haitian Creole, Lao, Zulu) developed within the second evaluation campaign of the IARPA Babel project. This year, the main focus of the project is put on training high-performing automatic speech recognition (ASR) and keyword search (KWS) systems from language resources limited to about 10 hours of transcribed speech data. Optimizing the structure of Multilayer Perceptron (MLP) based feature extraction and switching from the sigmoid activation function to rectified linear units results in about 5 relative improvement over baseline MLP features. Further improvements are obtained when the MLPs are trained on multiple feature streams and by exploiting label preserving data augmentation techniques like vocal tract length perturbation. Systematic application of these methods allows to improve the unilingual systems by 4-6 absolute in WER and 0.064-0.105 absolute in MTWV. Transfer and adaptation of multilingually trained MLPs lead to additional gains, clearly exceeding the project goal of 0.3 MTWV even when only the limited language pack of the target language is used. Index Terms: ASR, KWS, MTWV, MLP, rectified linear units, multilingual, low-resource When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random "dropout" gives big improvements on many benchmark tasks and sets new records for speech and object recognition. Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We present a detailed error analysis of existing taggers, motivating a series of tagger augmentations which are demonstrated to improve performance. We identify and evaluate techniques for improving English part-of-speech tagging performance in this genre. Further, we present a novel approach to system combination for the case where available taggers use different tagsets, based on voteconstrained bootstrapping with unlabeled data. Coupled with assigning prior probabilities to some tokens and handling of unknown words and slang, we reach 88.7 tagging accuracy (90.5 on development data). This is a new high in PTB-compatible tweet part-of-speech tagging, reducing token error by 26.8 and sentence error by 12.2 . The model, training data and tools are made available. Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment. Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset. This paper investigates data augmentation for deep neural network acoustic modeling based on label-preserving transformations to deal with data sparsity. Two data augmentation approaches, vocal tract length perturbation (VTLP) and stochastic feature mapping (SFM), are investigated for both deep neural networks (DNNs) and convolutional neural networks (CNNs). The approaches are focused on increasing speaker and speech variations of the limited training data such that the acoustic models trained with the augmented data are more robust to such variations. In addition, a two-stage data augmentation scheme based on a stacked architecture is proposed to combine VTLP and SFM as complementary approaches. Experiments are conducted on Assamese and Haitian Creole, two development languages of the IARPA Babel program, and improved performance on automatic speech recognition (ASR) and keyword search (KWS) is reported. A new training procedure called multi-style training has been developed to improve performance when a recognizer is used under stress or in high noise but cannot be trained in these conditions. Instead of speaking normally during training, talkers use different, easily produced, talking styles. This technique was tested using a speech data base that included stress speech produced during a workload task and when intense noise was presented through earphones. A continuous-distribution talker-dependent Hidden Markov Model (HMM) recognizer was trained both normally (5 normally spoken tokens) and with multi-style training (one token each from normal, fast, clear, loud, and question-pitch talking styles). The average error rate under stress and normal conditions fell by more than a factor of two with multi-style training and the average error rate under conditions sampled during training fell by a factor of four. We address the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90 accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.