Document Summarization

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.

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).

DynamicConv is a type of convolution for sequential modelling where it has kernels that vary over time as a learned function of the individual time steps. It builds upon LightConv and takes the same form but uses a time-step dependent kernel: $$ \text{DynamicConv}\left(X, i, c\right) = \text{LightConv}\left(X, f\left(X\_{i}\right)\_{h,:}, i, c\right) $$

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.

A Time-aware Large Kernel (TaLK) convolution is a type of temporal convolution that learns the kernel size of a summation kernel for each time-step instead of learning the kernel weights as in a typical convolution operation. For each time-step, a function is responsible for predicting the appropriate size of neighbor representations to use in the form of left and right offsets relative to the time-step.