Semantic Textual Similarity

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.

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.

Flan-T5 is the instruction fine-tuned version of T5 or Text-to-Text Transfer Transformer Language Model.

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.

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).

SimCSE is a contrastive learning framework for generating sentence embeddings. It utilizes an unsupervised approach, which takes an input sentence and predicts itself in contrastive objective, with only standard dropout used as noise. The authors find that dropout acts as minimal “data augmentation” of hidden representations, while removing it leads to a representation collapse. Afterwards a supervised approach is used, which incorporates annotated pairs from natural language inference datasets into the contrastive framework, by using “entailment” pairs as positives and “contradiction

In image inpainting task, the mechanism extracts complementary features from the word embedding in two paths by reciprocal attention, which is done by comparing the descriptive text and complementary image areas through reciprocal attention.

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).

MobileBERT is a type of inverted-bottleneck BERT that compresses and accelerates the popular BERT model. MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. It is trained by layer-to-layer imitating the inverted bottleneck BERT.

Mirror-BERT converts pretrained language models into effective universal text encoders without any supervision, in 20-30 seconds. It is an extremely simple, fast, and effective contrastive learning technique. It relies on fully identical or slightly modified string pairs as positive (i.e., synonymous) fine-tuning examples, and aims to maximise their similarity during identity fine-tuning.

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.

TSDAE is an unsupervised sentence embedding method. During training, TSDAE encodes corrupted sentences into fixed-sized vectors and requires the decoder to reconstruct the original sentences from this sentence embedding. For good reconstruction quality, the semantics must be captured well in the sentence embedding from the encoder. Later, at inference, we only use the encoder for creating sentence embeddings. The model architecture of TSDAE is a modified encoder-decoder Transformer where the key and value of the cross-attention are both confined to the sentence embedding only. Formally, the formulation of the modified cross-attention is: $$ H^{(k)}=\text { Attention }\left(H^{(k-1)},\left[s^{T}\right],\left[s^{T}\right]\right) $$ $$ \operatorname{Attention}(Q, K, V)=\operatorname{softmax}\left(\frac{Q K^{T}}{\sqrt{d}}\right) V $$ where $H^{(k)} \in \mathbb{R}^{t \times d}$ is the decoder hidden states within $t$ decoding steps at the $k$-th layer, $d$ is the size of the sentence embedding, $\left[s^{T}\right] \in \mathbb{R}^{1 \times d}$ is a one-row matrix including the sentence embedding vector and $Q, K$ and $V$ are the query, key and value, respectively. By exploring different configurations on the STS benchmark dataset, the authors discover that the best combination is: (1) adopting deletion as the input noise and setting the deletion ratio to $0.6,(2)$ using the output of the [CLS] token as fixed-sized sentence representation (3) tying the encoder and decoder parameters during training.

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.

Factorized Dense Synthesized Attention is a synthesized attention mechanism, similar to dense synthesized attention, but we factorize the outputs to reduce parameters and prevent overfitting. It was proposed as part of the Synthesizer architecture. The factorized variant of the dense synthesizer can be expressed as follows: $$A, B = F\_{A}\left(X\_{i}\right), F\_{B}\left(X\_{i}\right)$$ where $F\_{A}\left(.\right)$ projects input $X\_{i}$ into $a$ dimensions, $F\_B\left(.\right)$ projects $X\_{i}$ to $b$ dimensions, and $a \text{ x } b = l$. The output of the factorized module is now written as: $$ Y = \text{Softmax}\left(C\right)G\left(X\right) $$ where $C = H\_{A}\left(A\right) * H\_{B}\left(B\right)$, where $H\_{A}$, $H\_{B}$ are tiling functions and $C \in \mathbb{R}^{l \text{ x } l}$. The tiling function simply duplicates the vector $k$ times, i.e., $\mathbb{R}^{l} \rightarrow \mathbb{R}^{lk}$. In this case, $H\_{A}\left(\right)$ is a projection of $\mathbb{R}^{a} \rightarrow \mathbb{R}^{ab}$ and $H\_{B}\left(\right)$ is a projection of $\mathbb{R}^{b} \rightarrow \mathbb{R}^{ba}$. To avoid having similar values within the same block, we compose the outputs of $H\_{A}$ and $H\_{B}$.

Factorized Random Synthesized Attention, introduced with the Synthesizer architecture, is similar to factorized dense synthesized attention but for random synthesizers. Letting $R$ being a randomly initialized matrix, we factorize $R$ into low rank matrices $R\_{1}, R\_{2} \in \mathbb{R}^{l\text{ x}k}$ in the attention function: $$ Y = \text{Softmax}\left(R\_{1}R\_{2}^{T}\right)G\left(X\right) . $$ Here $G\left(.\right)$ is a parameterized function that is equivalent to $V$ in Scaled Dot-Product Attention. For each head, the factorization reduces the parameter costs from $l^{2}$ to $2\left(lk\right)$ where $k << l$ and hence helps prevent overfitting. In practice, we use a small value of $k = 8$. The basic idea of a Random Synthesizer is to not rely on pairwise token interactions or any information from individual token but rather to learn a task-specific alignment that works well globally across many samples.

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.

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.

Unsupervised knowledge distillation from a pretrained language model to itself, by alternating between its bi- and cross-encoder forms.