Efficient Robust Training via Backward Smoothing

Adversarial training is so far the most effective strategy in defending against adversarial examples. However, it suffers from high computational cost due to the iterative adversarial attacks in each training step. Recent studies show that it is possible to achieve Fast Adversarial Training by performing a single-step attack with random initialization. Yet, it remains a mystery why random initialization helps. Besides, such an approach still lags behind state-of-the-art adversarial training algorithms on both stability and model robustness. In this work, we develop a new understanding towards Fast Adversarial Training, by viewing random initialization as performing randomized smoothing for better optimization of the inner maximization problem. From this perspective, we show that the smoothing effect by random initialization is not sufficient under the adversarial perturbation constraint. A new initialization strategy, backward smoothing, is proposed to address this issue and significantly improves both stability and model robustness over single-step robust training methods.Experiments on multiple benchmarks demonstrate that our method achieves similar model robustness as the original TRADES method, while using much less training time ($\sim$3x improvement with the same training schedule).

Contrastive Distillation on Intermediate Representations for Language Model Compression

Existing language model compression methods mostly use a simple L2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. Although widely used, this objective by design assumes that all the dimensions of hidden representations are independent, failing to capture important structural knowledge in the intermediate layers of the teacher network. To achieve better distillation efficacy, we propose Contrastive Distillation on Intermediate Representations (CoDIR), a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective. By learning to distinguish positive sample from a large set of negative samples, CoDIR facilitates the student's exploitation of rich information in teacher's hidden layers. CoDIR can be readily applied to compress large-scale language models in both pre-training and finetuning stages, and achieves superb performance on the GLUE benchmark, outperforming state-of-the-art compression methods.

FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that is essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-lingual fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. For simple tasks such as classification, translated text in the target language shares the same label as the source language. However, this shared label becomes less accurate or even unavailable for more complex tasks such as question answering, NER and POS tagging. For better model scalability, we further propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language. Extensive experiments demonstrate that FILTER achieves new state of the art on two challenging multilingual multi-task benchmarks, XTREME and XGLUE.

Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens. However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically with respect to the sequence length. Therefore, long sequences are often encoded by Transformer in chunks using a sliding window. In this paper, we propose Cluster-Former, a novel clustering-based sparse Transformer to perform attention across chunked sequences. Our proposed method allows information integration beyond local windows, which is especially beneficial for question answering (QA) and language modeling tasks that rely on long-range dependencies. Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.

Accelerating Real-Time Question Answering via Question Generation

Existing approaches to real-time question answering (RTQA) rely on learning the representations of only key phrases in the documents, then matching them with the question representation to derive answer. However, such approach is bottlenecked by the encoding time of real-time questions, thus suffering from detectable latency in deployment for large-volume traffic. To accelerate RTQA for practical use, we present Ocean-Q (an Ocean of Questions), a novel approach that leverages question generation (QG) for RTQA. Ocean-Q introduces a QG model to generate a large pool of question-answer (QA) pairs offline, then in real time matches an input question with the candidate QA pool to predict the answer without question encoding. To further improve QG quality, we propose a new data augmentation method and leverage multi-task learning and diverse beam search to boost RTQA performance. Experiments on SQuAD(-open) and HotpotQA benchmarks demonstrate that Ocean-Q is able to accelerate the fastest state-of-the-art RTQA system by 4X times, with only a 3+% accuracy drop.

Graph Optimal Transport for Cross-Domain Alignment

Cross-domain alignment between two sets of entities (e.g., objects in an image, words in a sentence) is fundamental to both computer vision and natural language processing. Existing methods mainly focus on designing advanced attention mechanisms to simulate soft alignment, with no training signals to explicitly encourage alignment. The learned attention matrices are also dense and lacks interpretability. We propose Graph Optimal Transport (GOT), a principled framework that germinates from recent advances in Optimal Transport (OT). In GOT, cross-domain alignment is formulated as a graph matching problem, by representing entities into a dynamically-constructed graph. Two types of OT distances are considered: (i) Wasserstein distance (WD) for node (entity) matching; and (ii) Gromov-Wasserstein distance (GWD) for edge (structure) matching. Both WD and GWD can be incorporated into existing neural network models, effectively acting as a drop-in regularizer. The inferred transport plan also yields sparse and self-normalized alignment, enhancing the interpretability of the learned model. Experiments show consistent outperformance of GOT over baselines across a wide range of tasks, including image-text retrieval, visual question answering, image captioning, machine translation, and text summarization.

Adaptive Learning Rates with Maximum Variation Averaging

Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute element-wise adaptive step sizes and handle noisy gradients. However, Adam can have undesirable convergence behavior in some problems due to unstable or extreme adaptive learning rates. Methods such as AMSGrad and AdaBound have been proposed to stabilize the adaptive learning rates of Adam in the later stage of training, but they do not outperform Adam in some practical tasks such as training Transformers. In this paper, we propose an adaptive learning rate rule in which the running mean squared gradient is replaced by a weighted mean, with weights chosen to maximize the estimated variance of each coordinate. This gives a worst-case estimate for the local gradient variance, taking smaller steps when large curvatures or noisy gradients are present, resulting in more desirable convergence behavior than Adam. We analyze and demonstrate the improved efficacy of our adaptive averaging approach on image classification, neural machine translation and natural language understanding tasks.

Large-Scale Adversarial Training for Vision-and-Language Representation Learning

We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the "free" adversarial training strategy, and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR2.

Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models

Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. Models such as ViLBERT, LXMERT and UNITER have significantly lifted state of the art across a wide range of V+L benchmarks with joint image-text pre-training. However, little is known about the inner mechanisms that destine their impressive success. To reveal the secrets behind the scene of these powerful models, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e.g., Visual Coreference Resolution, Visual Relation Detection, Linguistic Probing Tasks) generalizable to standard pre-trained V+L models, aiming to decipher the inner workings of multimodal pre-training (e.g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings). Through extensive analysis of each archetypal model architecture via these probing tasks, our key observations are: (i) Pre-trained models exhibit a propensity for attending over text rather than images during inference. (ii) There exists a subset of attention heads that are tailored for capturing cross-modal interactions. (iii) Learned attention matrix in pre-trained models demonstrates patterns coherent with the latent alignment between image regions and textual words. (iv) Plotted attention patterns reveal visually-interpretable relations among image regions. (v) Pure linguistic knowledge is also effectively encoded in the attention heads. These are valuable insights serving to guide future work towards designing better model architecture and objectives for multimodal pre-training.