Semantic Parsing

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.

A Non-Local Operation is a component for capturing long-range dependencies with deep neural networks. It is a generalization of the classical non-local mean operation in computer vision. Intuitively a non-local operation computes the response at a position as a weighted sum of the features at all positions in the input feature maps. The set of positions can be in space, time, or spacetime, implying that these operations are applicable for image, sequence, and video problems. Following the non-local mean operation, a generic non-local operation for deep neural networks is defined as: $$ \mathbb{y}\_{i} = \frac{1}{\mathcal{C}\left(\mathbb{x}\right)}\sum\_{\forall{j}}f\left(\mathbb{x}\_{i}, \mathbb{x}\_{j}\right)g\left(\mathbb{x}\_{j}\right) $$ Here $i$ is the index of an output position (in space, time, or spacetime) whose response is to be computed and $j$ is the index that enumerates all possible positions. x is the input signal (image, sequence, video; often their features) and $y$ is the output signal of the same size as $x$. A pairwise function $f$ computes a scalar (representing relationship such as affinity) between $i$ and all $j$. The unary function $g$ computes a representation of the input signal at the position $j$. The response is normalized by a factor $C\left(x\right)$. The non-local behavior is due to the fact that all positions ($\forall{j}$) are considered in the operation. As a comparison, a convolutional operation sums up the weighted input in a local neighborhood (e.g., $i − 1 \leq j \leq i + 1$ in a 1D case with kernel size 3), and a recurrent operation at time $i$ is often based only on the current and the latest time steps (e.g., $j = i$ or $i − 1$). The non-local operation is also different from a fully-connected (fc) layer. The equation above computes responses based on relationships between different locations, whereas fc uses learned weights. In other words, the relationship between $x\_{j}$ and $x\_{i}$ is not a function of the input data in fc, unlike in nonlocal layers. Furthermore, the formulation in the equation above supports inputs of variable sizes, and maintains the corresponding size in the output. On the contrary, an fc layer requires a fixed-size input/output and loses positional correspondence (e.g., that from $x\_{i}$ to $y\_{i}$ at the position $i$). A non-local operation is a flexible building block and can be easily used together with convolutional/recurrent layers. It can be added into the earlier part of deep neural networks, unlike fc layers that are often used in the end. This allows us to build a richer hierarchy that combines both non-local and local information. In terms of parameterisation, we usually parameterise $g$ as a linear embedding of the form $g\left(x\_{j}\right) = W\_{g}\mathbb{x}\_{j}$ , where $W\_{g}$ is a weight matrix to be learned. This is implemented as, e.g., 1×1 convolution in space or 1×1×1 convolution in spacetime. For $f$ we use an affinity function, a list of which can be found here.

A Non-Local Block is an image block module used in neural networks that wraps a non-local operation. We can define a non-local block as: $$ \mathbb{z}\_{i} = W\_{z}\mathbb{y\_{i}} + \mathbb{x}\_{i} $$ where $y\_{i}$ is the output from the non-local operation and $+ \mathbb{x}\_{i}$ is a residual connection.

Embedded Gaussian Affinity is a type of affinity or self-similarity function between two points $\mathbf{x\_{i}}$ and $\mathbf{x\_{j}}$ that uses a Gaussian function in an embedding space: $$ f\left(\mathbf{x\_{i}}, \mathbf{x\_{j}}\right) = e^{\theta\left(\mathbf{x\_{i}}\right)^{T}\phi\left(\mathbf{x\_{j}}\right)} $$ Here $\theta\left(x\_{i}\right) = W\_{θ}x\_{i}$ and $\phi\left(x\_{j}\right) = W\_{φ}x\_{j}$ are two embeddings. Note that the self-attention module used in the original Transformer model is a special case of non-local operations in the embedded Gaussian version. This can be seen from the fact that for a given $i$, $\frac{1}{\mathcal{C}\left(\mathbf{x}\right)}\sum\_{\forall{j}}f\left(\mathbf{x}\_{i}, \mathbf{x}\_{j}\right)g\left(\mathbf{x}\_{j}\right)$ becomes the softmax computation along the dimension $j$. So we have $\mathbf{y} = \text{softmax}\left(\mathbf{x}^{T}W^{T}\_{\theta}W\_{\phi}\mathbf{x}\right)g\left(\mathbf{x}\right)$, which is the self-attention form in the Transformer model. This shows how we can relate this recent self-attention model to the classic computer vision method of non-local means.

TAPEX is a conceptually simple and empirically powerful pre-training approach to empower existing models with table reasoning skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesising executable SQL queries.

TaBERT is a pretrained language model (LM) that jointly learns representations for natural language sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In summary, TaBERT's process for learning representations for NL sentences is as follows: Given an utterance $u$ and a table $T$, TaBERT first creates a content snapshot of $T$. This snapshot consists of sampled rows that summarize the information in $T$ most relevant to the input utterance. The model then linearizes each row in the snapshot, concatenates each linearized row with the utterance, and uses the concatenated string as input to a Transformer model, which outputs row-wise encoding vectors of utterance tokens and cells. The encodings for all the rows in the snapshot are fed into a series of vertical self-attention layers, where a cell representation (or an utterance token representation) is computed by attending to vertically-aligned vectors of the same column (or the same NL token). Finally, representations for each utterance token and column are generated from a pooling layer.

Concatenation Affinity is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a concatenation function: $$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \text{ReLU}\left(\mathbb{w}^{T}\_{f}\left[\theta\left(\mathbb{x}\_{i}\right), \phi\left(\mathbb{x}\_{j}\right)\right]\right)$$ Here $\left[·, ·\right]$ denotes concatenation and $\mathbb{w}\_{f}$ is a weight vector that projects the concatenated vector to a scalar.

A brand new Foundation Model framework for deep learning in the future.

PSFR-GAN is a semantic-aware style transformation framework for face restoration. Given a pair of LQ face image and its corresponding parsing map, we first generate a multi-scale pyramid of the inputs, and then progressively modulate different scale features from coarse-to-fine in a semantic-aware style transfer way. Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of inputs.

ReasonBERT is a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid, contexts. It utilizes distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning. Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases. Specifically, given a query sentence containing an entity pair, if we mask one of the entities, another sentence or table that contains the same pair of entities can likely be used as evidence to recover the masked entity. Moreover, to encourage deeper reasoning, multiple pieces of evidence are collected that are jointly used to recover the masked entities in the query sentence, allowing for the scattering of the masked entities among different pieces of evidence to mimic different types of reasoning. The Figure illustrates several examples using such distant supervision. In Ex. 1, a model needs to check multiple constraints (i.e., intersection reasoning type) and find “the beach soccer competition that is established in 1998.” In Ex. 2, a model needs to find “the type of the band that released Awaken the Guardian,” by first inferring the name of the band “Fates Warning” (i.e., bridging reasoning type). The masked entities in a query sentence are replaced with the [QUESTION] tokens. The new pre-training objective, span reasoning, then extracts the masked entities from the provided evidence. Existing LMs like BERT and RoBERTa are augmented by continuing to train them with the new objective, which leads to ReasonBERT. Then query sentence and textual evidence are encoded via the LM. When tabular evidence is present, the structure-aware transformer TAPAS is used as the encoder to capture the table structure.