Z ero S hot C eres: Zero-Shot Relation Extraction from Semi-Structured Webpages (2020)

Abstract: In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for “zero-shot” open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.

## introduction semi-structured websites offer rich sources of highquality data across many areas of knowledge @xcite . these websites present information via text that is accompanied by rich visual and layout features that can be generalized beyond a single website. however, most prior work on information extraction (ie) from websites has largely ignored most of these features, instead relying only on html features specific to an individual website @xcite . this requires training data for every website targeted for extraction, an approach that cannot scale up if training data must be manually created. to circumvent manual data annotation, previous work used a distant supervision process requiring a knowledge base aligned to the website targeted for extraction @xcite , including for openie extraction @xcite @xcite . these methods, however, can only learn a website-specific model based on seed knowledge for the site, but cannot be generalized to the majority of websites with knowledge from new verticals, by long-tail specialists, and in different languages. in this paper, we introduce the task of zero-shot relation extraction from semi-structured websites, in which a learned model is applied to extract from a website that was not represented in its training data (figure 1 ). moreover, we introduce ze-roshotceres, a graph neural network model that encodes semantic textual and visual patterns common across different training websites and can generalize to extract information from documents with never-before-seen templates and topics. unlike unstructured text, which can be modeled as a sequence, or images, which can be modeled as a two-dimensional grid of pixels, it is not obvious how to operate over the many shapes and sizes of text fields on a semi-structured webpage. we illustrate our intuition using the webpage snippets in figure 1 : despite their differences, each site uses alignment of relation and object strings, either vertically or horizontally, to help indicate relationships; in addition, relation strings are often more prominent than their objects, either in size or boldness. such features are semantically meaningful to readers and often consistent from site to site; thus, encoding them into the representation of webpages will allow us to generalize to unseen sites. our model, zeroshotceres, encodes these diverse feature types in a graph representation in which each text field becomes a node in a graph, connected by edges indicating layout relationships on the page. this abstracts away the details of the page while maintaining the core visual structure presented to the reader. a graph neural network is then applied to produce a new representation of each text field, informed by the surrounding page context. this representation is then used to extract entities and relationships from the document. this allows us to extract not only in the closed-domain setting, but also allows us to conduct openie on websites about entirely new subject verticals not seen during training. our contributions are threefold: (a) we introduce a graph neural network model for webpage representation that integrates multi-modal information including visual, layout, and textual features, enabling generalization for ie from never-beforeseen websites. (b) we propose the first approach to enable open information extraction from semistructured websites without prior knowledge or training data in the subject vertical. (c) our method works in both openie and closedie settings. we conduct evaluations showing the effectiveness of the technique and exploring the challenges of zeroshot semi-structured ie, achieving a 31% improvement in f1 compared to an openie baseline. the graph model gives a 26% f1 boost when extracting according to a defined schema (closedie). dom-based openie: weir @xcite and openceres @xcite offer openie approaches to dom extraction. the latter method uses visual features in a semi-supervised learning setting to identify candidate pairs that are visually similar to known (relation, object) pairs; however, the ultimate extraction model learned is still sitespecific and based on dom features rather than the more generalizable visual or textual features. pasupat and liang (2014) present a zero-shot method for extraction from semi-structured webpages, but limit their work to extraction of entities rather than relationships and do not consider visual elements of the page. ## relation extraction model once we have the new representation h l t of each text field t produced by the above gnn process, we can perform our final classification. ## conclusion we have introduced a zero-shot method for learning a model for relation extraction from semistructured documents that generalizes beyond a single document template. moreover, this approach enables openie extraction from entirely new subject verticals where no prior knowledge is available. by representing a webpage as a graph defined by layout relationship between text fields, with text fields associated with both visual and textual features, we attain a 31% improvement over the baseline for new-vertical openie extraction. future extensions of this work involve a more general pre-training objective allowing for the learned representations to be useful in many tasks as well as distantly or semi-supervised approaches to benefit from more data.