Language Modelling

Fraternal Dropout is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.

G3D is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.

MacBERT is a Transformer-based model for Chinese NLP that alters RoBERTa in several ways, including a modified masking strategy. Instead of masking with [MASK] token, which never appears in the fine-tuning stage, MacBERT masks the word with its similar word. Specifically MacBERT shares the same pre-training tasks as BERT with several modifications. For the MLM task, the following modifications are performed: - Whole word masking is used as well as Ngram masking strategies for selecting candidate tokens for masking, with a percentage of 40%, 30%, 20%, 10% for word-level unigram to 4-gram. - Instead of masking with [MASK] token, which never appears in the fine-tuning stage, similar words are used for the masking purpose. A similar word is obtained by using Synonyms toolkit which is based on word2vec similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement. - A percentage of 15% input words is used for masking, where 80% will replace with similar words, 10% replace with a random word, and keep with original words for the rest of 10%.

MaxUp is an adversarial data augmentation technique for improving the generalization performance of machine learning models. The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data. By doing so, we implicitly introduce a smoothness or robustness regularization against the random perturbations, and hence improve the generation performance. For example, in the case of Gaussian perturbation, MaxUp is asymptotically equivalent to using the gradient norm of the loss as a penalty to encourage smoothness.

Mesh-TensorFlow is a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A MeshTensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce.

The Mogrifier LSTM is an extension to the LSTM where the LSTM’s input $\mathbf{x}$ is gated conditioned on the output of the previous step $\mathbf{h}\_{prev}$. Next, the gated input is used in a similar manner to gate the output of the previous time step. After a couple of rounds of this mutual gating, the last updated $\mathbf{x}$ and $\mathbf{h}\_{prev}$ are fed to an LSTM. In detail, the Mogrifier is an LSTM where two inputs $\mathbf{x}$ and $\mathbf{h}\_{prev}$ modulate one another in an alternating fashion before the usual LSTM computation takes place. That is: $ \text{Mogrify}\left(\mathbf{x}, \mathbf{c}\_{prev}, \mathbf{h}\_{prev}\right) = \text{LSTM}\left(\mathbf{x}^{↑}, \mathbf{c}\_{prev}, \mathbf{h}^{↑}\_{prev}\right)$ where the modulated inputs $\mathbf{x}^{↑}$ and $\mathbf{h}^{↑}\_{prev}$ are defined as the highest indexed $\mathbf{x}^{i}$ and $\mathbf{h}^{i}\_{prev}$, respectively, from the interleaved sequences: $$ \mathbf{x}^{i} = 2\sigma\left(\mathbf{Q}^{i}\mathbf{h}^{i−1}\_{prev}\right) \odot x^{i-2} \text{ for odd } i \in \left[1 \dots r\right] $$ $$ \mathbf{h}^{i}\_{prev} = 2\sigma\left(\mathbf{R}^{i}\mathbf{x}^{i-1}\right) \odot \mathbf{h}^{i-2}\_{prev} \text{ for even } i \in \left[1 \dots r\right] $$ with $\mathbf{x}^{-1} = \mathbf{x}$ and $\mathbf{h}^{0}\_{prev} = \mathbf{h}\_{prev}$. The number of "rounds", $r \in \mathbb{N}$, is a hyperparameter; $r = 0$ recovers the LSTM. Multiplication with the constant 2 ensures that randomly initialized $\mathbf{Q}^{i}$, $\mathbf{R}^{i}$ matrices result in transformations close to identity. To reduce the number of additional model parameters, we typically factorize the $\mathbf{Q}^{i}$, $\mathbf{R}^{i}$ matrices as products of low-rank matrices: $\mathbf{Q}^{i}$ = $\mathbf{Q}^{i}\_{left}\mathbf{Q}^{i}\_{right}$ with $\mathbf{Q}^{i} \in \mathbb{R}^{m\times{n}}$, $\mathbf{Q}^{i}\_{left} \in \mathbb{R}^{m\times{k}}$, $\mathbf{Q}^{i}\_{right} \in \mathbb{R}^{k\times{n}}$, where $k < \min\left(m, n\right)$ is the rank.

Pixel-BERT is a pre-trained model trained to align image pixels with text. The end-to-end framework includes a CNN-based visual encoder and cross-modal transformers for visual and language embedding learning. This model has three parts: one fully convolutional neural network that takes pixels of an image as input, one word-level token embedding based on BERT, and a multimodal transformer for jointly learning visual and language embedding. For language, it uses other pretraining works to use Masked Language Modeling (MLM) to predict masked tokens with surrounding text and images. For vision, it uses the random pixel sampling mechanism that makes up for the challenge of predicting pixel-level features. This mechanism is also suitable for solving overfitting issues and improving the robustness of visual features. It applies Image-Text Matching (ITM) to classify whether an image and a sentence pair match for vision and language interaction. Image captioning is required to understand language and visual semantics for cross-modality tasks like VQA. Region-based visual features extracted from object detection models like Faster RCNN are used for better performance in the newer version of the model.

SHA-RNN, or Single Headed Attention RNN, is a recurrent neural network, and language model when combined with an embedding input and softmax classifier, based on a core LSTM component and a single-headed attention module. Other design choices include a Boom feedforward layer and the use of layer normalization. The guiding principles of the author were to ensure simplicity in the architecture and to keep computational costs bounded (the model was originally trained with a single GPU).

SRU++ is a self-attentive recurrent unit that combines fast recurrence and attention for sequence modeling, extending the SRU unit. The key modification of SRU++ is to incorporate more expressive non-linear operations into the recurrent network. Specifically, given the input sequence represented as a matrix $\mathbf{X} \in \mathbb{R}^{L \times d}$, the attention component computes the query, key and value representations using the following multiplications, $$ \mathbf{Q} =\mathbf{W}^{q} \mathbf{X}^{\top} $$ $$ \mathbf{K} =\mathbf{W}^{k} \mathbf{Q} \\ $$ $$ \mathbf{V} =\mathbf{W}^{v} \mathbf{Q} $$ where $\mathbf{W}^{q} \in \mathbb{R}^{d^{\prime} \times d}, \mathbf{W}^{k}, \mathbf{W}^{v} \in \mathbb{R}^{d^{\prime} \times d^{\prime}}$ are model parameters. $d^{\prime}$ is the attention dimension that is typically much smaller than $d$. Note that the keys $\mathbf{K}$ and values $\mathbf{V}$ are computed using $\mathbf{Q}$ instead of $\mathbf{X}$ such that the weight matrices $\mathbf{W}^{k}$ and $\mathbf{W}^{v}$ are significantly smaller. Next, we compute a weighted average output $\mathbf{A} \in \mathbb{R}^{d^{\prime} \times L}$ using scaled dot-product attention: $$ \mathbf{A}^{\top}=\operatorname{softmax}\left(\frac{\mathbf{Q}^{\top} \mathbf{K}}{\sqrt{d^{\prime}}}\right) \mathbf{V}^{\top} $$ The final output $U$ required by the elementwise recurrence is obtained by another linear projection, $$ \mathbf{U}^{\top}=\mathbf{W}^{o}(\mathbf{Q}+\alpha \cdot \mathbf{A}) $$ where $\alpha \in \mathbb{R}$ is a learned scalar and $\mathbf{W}\_{o} \in \mathbb{R}^{3 d \times d^{\prime}}$ is a parameter matrix. $\mathbf{Q}+\alpha \cdot \mathbf{A}$ is a residual connection which improves gradient propagation and stabilizes training. We initialize $\alpha$ to zero and as a result, $$ \mathbf{U}^{\top}=\mathbf{W}^{o} \mathbf{Q}=\left(\mathbf{W}^{o} \mathbf{W}^{q}\right) \mathbf{X}^{\top} $$ initially falls back to a linear transformation of the input $X$ skipping the attention transformation. Intuitively, skipping attention encourages leveraging recurrence to capture sequential patterns during early stage of training. As $|\alpha|$ grows, the attention mechanism can learn long-range dependencies for the model. In addition, $\mathbf{W}^{o} \mathbf{W}^{q}$ can be interpreted as applying a matrix factorization trick with a small inner dimension $d^{\prime}<d$, reducing the total number of parameters. The Figure compares the differences of SRU, SRU with this factorization trick (but without attention), and SRU++. The last modification is adding layer normalization to each SRU++ layer. We apply normalization after the attention operation and before the matrix multiplication with $\mathbf{W}^{o}$ $$ \mathbf{U}^{\top}=\mathbf{W}^{o} \operatorname{layernorm}(\mathbf{Q}+\alpha \cdot \mathbf{A}) $$ This implementation is post-layer normalization in which the normalization is added after the residual connection.

A Sandwich Transformer is a variant of a Transformer that reorders sublayers in the architecture to achieve better performance. The reordering is based on the authors' analysis that models with more self-attention toward the bottom and more feedforward sublayers toward the top tend to perform better in general.

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Sparse Sinkhorn Attention is an attention mechanism that reduces the memory complexity of the dot-product attention mechanism and is capable of learning sparse attention outputs. It is based on the idea of differentiable sorting of internal representations within the self-attention module. SSA incorporates a meta sorting network that learns to rearrange and sort input sequences. Sinkhorn normalization is used to normalize the rows and columns of the sorting matrix. The actual SSA attention mechanism then acts on the block sorted sequences.

Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP imposes a bidirectional constraints on global representation of entire images and sentences. Although IC conducts an unidirectional image-to-text generation on local representation, it lacks any constraint on local text-to-image reconstruction, which limits the ability to understand images at a fine-grained level when aligned with texts. To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels. Specifically, we expand a Text-Guided Masked Image Modeling (TG-MIM) head based on ITC and IC heads. The improved SyCoCa can further leverage textual cues to reconstruct contextual images and visual cues to predict textual contents. When implementing bidirectional local interactions, the local contents of images tend to be cluttered or unrelated to their textual descriptions. Thus, we employ an attentive masking strategy to select effective image patches for interaction. Extensive experiments on five vision-language tasks, including image-text retrieval, image-captioning, visual question answering, and zero-shot/finetuned image classification, validate the effectiveness of our proposed method.

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Augmented SBERT is a data augmentation strategy for pairwise sentence scoring that uses a BERT cross-encoder to improve the performance for the SBERT bi-encoders. Given a pre-trained, well-performing crossencoder, we sample sentence pairs according to a certain sampling strategy and label these using the cross-encoder. We call these weakly labeled examples the silver dataset and they will be merged with the gold training dataset. We then train the bi-encoder on this extended training dataset.

The BP-Transformer (BPT) is a type of Transformer that is motivated by the need to find a better balance between capability and computational complexity for self-attention. The architecture partitions the input sequence into different multi-scale spans via binary partitioning (BP). It incorporates an inductive bias of attending the context information from fine-grain to coarse-grain as the relative distance increases. The farther the context information is, the coarser its representation is. BPT can be regard as graph neural network, whose nodes are the multi-scale spans. A token node can attend the smaller-scale span for the closer context and the larger-scale span for the longer distance context. The representations of nodes are updated with Graph Self-Attention.

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CPT, or Chinese Pre-trained Unbalanced Transformer, is a pre-trained unbalanced Transformer for Chinese natural language understanding (NLU) and natural language generation (NLG) tasks. CPT consists of three parts: a shared encoder, an understanding decoder, and a generation decoder. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model.

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DExTra, or Deep and Light-weight Expand-reduce Transformation, is a light-weight expand-reduce transformation that enables learning wider representations efficiently. DExTra maps a $d\_{m}$ dimensional input vector into a high dimensional space (expansion) and then reduces it down to a $d\_{o}$ dimensional output vector (reduction) using $N$ layers of group transformations. During these expansion and reduction phases, DExTra uses group linear transformations because they learn local representations by deriving the output from a specific part of the input and are more efficient than linear transformations. To learn global representations, DExTra shares information between different groups in the group linear transformation using feature shuffling Formally, the DExTra transformation is controlled by five configuration parameters: (1) depth $N$, (2) width multiplier $m\_{w}$, (3) input dimension $d\_{m}$, (4) output dimension $d\_{o}$, and (5) maximum groups $g\_{max}$ in a group linear transformation. In the expansion phase, DExTra projects the $d\_{m}$-dimensional input to a high-dimensional space, $d\_{max} = m\_{w}d\_{m}$, linearly using $\text{ceil}\left(\frac{N}{2}\right)$ layers. In the reduction phase, DExTra projects the $d\_{max}$-dimensional vector to a $d\_{o}$-dimensional space using the remaining $N -\text{ceil}\left(\frac{N}{2}\right)$ layers. Mathematically, we define the output $Y$ at each layer $l$ as: $$ \mathbf{Y}\_{l} = \mathcal{F}\left(\mathbf{X}, \mathbf{W}^{l}, \mathbf{b}^{l}, g^{l}\right) \text{ if } l=1 $$ $$ \mathbf{Y}\_{l} = \mathcal{F}\left(\mathcal{H}\left(\mathbf{X}, \mathbf{Y}^{l-1}\right), \mathbf{W}^{l}, \mathbf{b}^{l}, g^{l}\right) \text{ Otherwise } $$ where the number of groups at each layer $l$ are computed as: $$ g^{l} = \text{min}\left(2^{l-1}, g\_{max}\right), 1 \leq l \leq \text{ceil}\left(N/2\right) $$ $$ g^{N-l}, \text{Otherwise}$$ In the above equations, $\mathcal{F}$ is a group linear transformation function. The function $\mathcal{F}$ takes the input $\left(\mathbf{X} \text{ or } \mathcal{H}\left(\mathbf{X}, \mathbf{Y}^{l-1}\right) \right)$, splits it into $g^{l}$ groups, and then applies a linear transformation with learnable parameters $\mathbf{W}^{l}$ and bias $\mathbf{b}^{l}$ to each group independently. The outputs of each group are then concatenated to produce the final output $\mathbf{Y}^{l}$. The function $\mathcal{H}$ first shuffles the output of each group in $\mathbf{Y}^{l−1}$ and then combines it with the input $\mathbf{X}$ using an input mixer connection. In the authors' experiments, they use $g\_{max} = \text{ceil}\left(\frac{d\_{m}}{32}\right)$ so that each group has at least 32 input elements. Note that (i) group linear transformations reduce to linear transformations when $g^{l} = 1$, and (ii) DExTra is equivalent to a multi-layer perceptron when $g\_{max} = 1$.

DeLiGHT is a transformer architecture that delivers parameter efficiency improvements by (1) within each Transformer block using DExTra, a deep and light-weight transformation, allowing for the use of single-headed attention and bottleneck FFN layers and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.

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