We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| wenzhihao666/g2p2-conditional | canonical | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| multitask_data_generator | Ran | wenzhihao666/g2p2-conditional/meta_net/task_amazon.py code served (permissive licence) · get_code("1fa3d8ef0eb40931") |
| multitask_data_generator | Ran | wenzhihao666/g2p2-conditional/meta_net/task_cora.py code served (permissive licence) · get_code("ce388f4c6324f5b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively. Moreover, we explore the possibility of employing continuous prompt tuning for zero-shot inference. Specifically, we aim to generalize continuous prompts to unseen classes while leveraging a set of base classes. To this end, we extend G2P2 into G2P2$^*$, hinging on a new architecture of conditional prompt tuning. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks, and illustrate the advantage of G2P2$^*$ in dealing with unseen classes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.10230")
get_code_for_paper("2307.10230")
have("2307.10230")
Connect an agent — have() is free.