Sentiment Analysis

BERT, or Bidirectional Encoder Representations from Transformers, improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. Unlike left-to-right language model pre-training, the MLM objective enables the representation to fuse the left and the right context, which allows us to pre-train a deep bidirectional Transformer. In addition to the masked language model, BERT uses a next sentence prediction task that jointly pre-trains text-pair representations. There are two steps in BERT: pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters.

Discriminative Fine-Tuning is a fine-tuning strategy that is used for ULMFiT type models. Instead of using the same learning rate for all layers of the model, discriminative fine-tuning allows us to tune each layer with different learning rates. For context, the regular stochastic gradient descent (SGD) update of a model’s parameters $\theta$ at time step $t$ looks like the following (Ruder, 2016): $$ \theta\_{t} = \theta\_{t-1} − \eta\cdot\nabla\_{\theta}J\left(\theta\right)$$ where $\eta$ is the learning rate and $\nabla\_{\theta}J\left(\theta\right)$ is the gradient with regard to the model’s objective function. For discriminative fine-tuning, we split the parameters $\theta$ into {$\theta\_{1}, \ldots, \theta\_{L}$} where $\theta\_{l}$ contains the parameters of the model at the $l$-th layer and $L$ is the number of layers of the model. Similarly, we obtain {$\eta\_{1}, \ldots, \eta\_{L}$} where $\theta\_{l}$ where $\eta\_{l}$ is the learning rate of the $l$-th layer. The SGD update with discriminative finetuning is then: $$ \theta\_{t}^{l} = \theta\_{t-1}^{l} - \eta^{l}\cdot\nabla\_{\theta^{l}}J\left(\theta\right) $$ The authors find that empirically it worked well to first choose the learning rate $\eta^{L}$ of the last layer by fine-tuning only the last layer and using $\eta^{l-1}=\eta^{l}/2.6$ as the learning rate for lower layers.

T5, or Text-to-Text Transfer Transformer, is a Transformer based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to BERT include: - adding a causal decoder to the bidirectional architecture. - replacing the fill-in-the-blank cloze task with a mix of alternative pre-training tasks.

Embeddings from Language Models, or ELMo, is a type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. A biLM combines both a forward and backward LM. ELMo jointly maximizes the log likelihood of the forward and backward directions. To add ELMo to a supervised model, we freeze the weights of the biLM and then concatenate the ELMo vector $\textbf{ELMO}^{task}_k$ with $\textbf{x}_k$ and pass the ELMO enhanced representation $[\textbf{x}_k; \textbf{ELMO}^{task}_k]$ into the task RNN. Here $\textbf{x}_k$ is a context-independent token representation for each token position. Image Source: here

Variational Dropout is a regularization technique based on dropout, but uses a variational inference grounded approach. In Variational Dropout, we repeat the same dropout mask at each time step for both inputs, outputs, and recurrent layers (drop the same network units at each time step). This is in contrast to ordinary Dropout where different dropout masks are sampled at each time step for the inputs and outputs alone.

DeBERTa is a Transformer-based neural language model that aims to improve the BERT and RoBERTa models with two techniques: a disentangled attention mechanism and an enhanced mask decoder. The disentangled attention mechanism is where each word is represented unchanged using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangle matrices on their contents and relative positions. The enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve model’s generalization on downstream tasks.

Embedding Dropout is equivalent to performing dropout on the embedding matrix at a word level, where the dropout is broadcast across all the word vector’s embedding. The remaining non-dropped-out word embeddings are scaled by $\frac{1}{1-p\_{e}}$ where $p\_{e}$ is the probability of embedding dropout. As the dropout occurs on the embedding matrix that is used for a full forward and backward pass, this means that all occurrences of a specific word will disappear within that pass, equivalent to performing variational dropout on the connection between the one-hot embedding and the embedding lookup. Source: Merity et al, Regularizing and Optimizing LSTM Language Models

ERNIE is a transformer-based model consisting of two stacked modules: 1) textual encoder and 2) knowledgeable encoder, which is responsible to integrate extra token-oriented knowledge information into textual information. This layer consists of stacked aggregators, designed for encoding both tokens and entities as well as fusing their heterogeneous features. To integrate this layer of enhancing representations via knowledge, a special pre-training task is adopted for ERNIE - it involves randomly masking token-entity alignments and training the model to predict all corresponding entities based on aligned tokens (aka denoising entity auto-encoder).

Slanted Triangular Learning Rates (STLR) is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period.

Universal Language Model Fine-tuning, or ULMFiT, is an architecture and transfer learning method that can be applied to NLP tasks. It involves a 3-layer AWD-LSTM architecture for its representations. The training consists of three steps: 1) general language model pre-training on a Wikipedia-based text, 2) fine-tuning the language model on a target task, and 3) fine-tuning the classifier on the target task. As different layers capture different types of information, they are fine-tuned to different extents using discriminative fine-tuning. Training is performed using Slanted triangular learning rates (STLR), a learning rate scheduling strategy that first linearly increases the learning rate and then linearly decays it. Fine-tuning the target classifier is achieved in ULMFiT using gradual unfreezing. Rather than fine-tuning all layers at once, which risks catastrophic forgetting, ULMFiT gradually unfreezes the model starting from the last layer (i.e., closest to the output) as this contains the least general knowledge. First the last layer is unfrozen and all unfrozen layers are fine-tuned for one epoch. Then the next group of frozen layers is unfrozen and fine-tuned and repeat, until all layers are fine-tuned until convergence at the last iteration.

A Dynamic Memory Network is a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN consists of a number of modules: - Input Module: The input module encodes raw text inputs from the task into distributed vector representations. The input takes forms like a sentence, a long story, a movie review and so on. - Question Module: The question module encodes the question of the task into a distributed vector representation. For question answering, the question may be a sentence such as "Where did the author first fly?". The representation is fed into the episodic memory module, and forms the basis, or initial state, upon which the episodic memory module iterates. - Episodic Memory Module: Given a collection of input representations, the episodic memory module chooses which parts of the inputs to focus on through the attention mechanism. It then produces a ”memory” vector representation taking into account the question as well as the previous memory. Each iteration provides the module with newly relevant information about the input. In other words, the module has the ability to retrieve new information, in the form of input representations, which were thought to be irrelevant in previous iterations. - Answer Module: The answer module generates an answer from the final memory vector of the memory module.

BigBird is a Transformer with a sparse attention mechanism that reduces the quadratic dependency of self-attention to linear in the number of tokens. BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. In particular, BigBird consists of three main parts: - A set of $g$ global tokens attending on all parts of the sequence. - All tokens attending to a set of $w$ local neighboring tokens. - All tokens attending to a set of $r$ random tokens. This leads to a high performing attention mechanism scaling to much longer sequence lengths (8x).

CoVe, or Contextualized Word Vectors, uses a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation to contextualize word vectors. $\text{CoVe}$ word embeddings are therefore a function of the entire input sequence. These word embeddings can then be used in downstream tasks by concatenating them with $\text{GloVe}$ embeddings: $$ v = \left[\text{GloVe}\left(x\right), \text{CoVe}\left(x\right)\right]$$ and then feeding these in as features for the task-specific models.

Spatial Gating Unit, or SGU, is a gating unit used in the gMLP architecture to captures spatial interactions. To enable cross-token interactions, it is necessary for the layer $s(\cdot)$ to contain a contraction operation over the spatial dimension. The layer $s(\cdot)$ is formulated as the output of linear gating: $$ s(Z)=Z \odot f\_{W, b}(Z) $$ where $\odot$ denotes element-wise multiplication. For training stability, the authors find it critical to initialize $W$ as near-zero values and $b$ as ones, meaning that $f\_{W, b}(Z) \approx 1$ and therefore $s(Z) \approx Z$ at the beginning of training. This initialization ensures each gMLP block behaves like a regular FFN at the early stage of training, where each token is processed independently, and only gradually injects spatial information across tokens during the course of learning. The authors find it further effective to split $Z$ into two independent parts $\left(Z\_{1}, Z\_{2}\right)$ along the channel dimension for the gating function and for the multiplicative bypass: $$ s(Z)=Z\_{1} \odot f\_{W, b}\left(Z\_{2}\right) $$ They also normalize the input to $f\_{W, b}$ which empirically improved the stability of large NLP models.

gMLP is an MLP-based alternative to Transformers without self-attention, which simply consists of channel projections and spatial projections with static parameterization. It is built out of basic MLP layers with gating. The model consists of a stack of $L$ blocks with identical size and structure. Let $X \in \mathbb{R}^{n \times d}$ be the token representations with sequence length $n$ and dimension $d$. Each block is defined as: $$ Z=\sigma(X U), \quad \tilde{Z}=s(Z), \quad Y=\tilde{Z} V $$ where $\sigma$ is an activation function such as GeLU. $U$ and $V$ define linear projections along the channel dimension - the same as those in the FFNs of Transformers (e.g., their shapes are $768 \times 3072$ and $3072 \times 768$ for $\text{BERT}_{\text {base }}$). A key ingredient is $s(\cdot)$, a layer which captures spatial interactions. When $s$ is an identity mapping, the above transformation degenerates to a regular FFN, where individual tokens are processed independently without any cross-token communication. One of the major focuses is therefore to design a good $s$ capable of capturing complex spatial interactions across tokens. This leads to the use of a Spatial Gating Unit which involves a modified linear gating. The overall block layout is inspired by inverted bottlenecks, which define $s(\cdot)$ as a spatial depthwise convolution. Note, unlike Transformers, gMLP does not require position embeddings because such information will be captured in $s(\cdot)$.

Charformer is a type of Transformer model that learns a subword tokenization end-to-end as part of the model. Specifically it uses GBST that automatically learns latent subword representations from characters in a data-driven fashion. Following GBST, the soft subword sequence is passed through Transformer layers.

GBST, or Gradient-based Subword Tokenization Module, is a soft gradient-based subword tokenization module that automatically learns latent subword representations from characters in a data-driven fashion. Concretely, GBST enumerates candidate subword blocks and learns to score them in a position-wise fashion using a block scoring network. GBST learns a position-wise soft selection over candidate subword blocks by scoring them with a scoring network. In contrast to prior tokenization-free methods, GBST learns interpretable latent subwords, which enables easy inspection of lexical representations and is more efficient than other byte-based models.

CTAL is a pre-training framework for strong audio-and-language representations with a Transformer, which aims to learn the intra-modality and inter-modalities connections between audio and language through two proxy tasks on a large amount of audio- and-language pairs: masked language modeling and masked cross-modal acoustic modeling. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.

Multi-source Sentiment Generative Adversarial Network is a multi-source domain adaptation (MDA) method for visual sentiment classification. It is composed of three pipelines, i.e., image reconstruction, image translation, and cycle-reconstruction. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Notably, thanks to the unified sentiment latent space, MSGAN requires a single classification network to handle data from different source domains.

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.

A novel low-resource intrinsic metric to evaluate word embedding quality based on graph modularity.

DistanceNet is a learning algorithm for multi-source domain adaptation that uses various distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.

PAR Transformer is a Transformer model that uses 63% fewer self-attention blocks, replacing them with feed-forward blocks, while retaining test accuracies. It is based on the Transformer-XL architecture and uses neural architecture search to find an an efficient pattern of blocks in the transformer architecture.

RealFormer is a type of Transformer based on the idea of residual attention. It adds skip edges to the backbone Transformer to create multiple direct paths, one for each type of attention module. It adds no parameters or hyper-parameters. Specifically, RealFormer uses a Post-LN style Transformer as backbone and adds skip edges to connect Multi-Head Attention modules in adjacent layers.

SKEP is a self-supervised pre-training method for sentiment analysis. With the help of automatically-mined knowledge, SKEP conducts sentiment masking and constructs three sentiment knowledge prediction objectives, so as to embed sentiment information at the word, polarity and aspect level into pre-trained sentiment representation. In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair. SKEP contains two parts: (1) Sentiment masking recognizes the sentiment information of an input sequence based on automatically-mined sentiment knowledge, and produces a corrupted version by removing these informations. (2) Sentiment pre-training objectives require the transformer to recover the removed information from the corrupted version. The three prediction objectives on top are jointly optimized: Sentiment Word (SW) prediction (on $\left.\mathrm{x}\_{9}\right)$, Word Polarity (SP) prediction (on $\mathrm{x}\_{6}$ and $\mathbf{x}\_{9}$ ), Aspect-Sentiment pairs (AP) prediction (on $\mathbf{x}\_{1}$ ). Here, the smiley denotes positive polarity. Notably, on $\mathrm{x}\_{6}$, only SP is calculated without SW, as its original word has been predicted in the pair prediction on $\mathbf{x}\_{1}$.

SqueezeBERT is an efficient architectural variant of BERT for natural language processing that uses grouped convolutions. It is much like BERT-base, but with positional feedforward connection layers implemented as convolutions, and grouped convolution for many of the layers.

It is often desirable to distill the capabilities of large language models (LLMs) into smaller student models due to compute and memory constraints. One way to do this for classification tasks is via dataset synthesis. Prior approaches to synthesis use few-shot prompting, which relies on the LLM's parametric knowledge to generate usable examples. However, this leads to issues of repetition, bias towards popular entities, and stylistic differences from human text. We propose Synthesize by Retrieval and Refinement (SynthesizRR), which uses retrieval augmentation to introduce variety into the dataset synthesis process: as retrieved passages vary, the LLM is seeded with different content to generate its examples. We find that SynthesizRR greatly improves lexical and semantic diversity, similarity to human-written text, and distillation performance,