Ning Xu

Ning Xu, Yifei Gao, Hongshuo Tian et al.

In the fundamental statistics course, students are taught to remember the well-known saying: "Correlation is not Causation". Till now, statistics (i.e., correlation) have developed various successful frameworks, such as Transformer and Pre-training large-scale models, which have stacked multiple parallel self-attention blocks to imitate a wide range of tasks. However, in the causation community, how to build an integrated causal framework still remains an untouched domain despite its excellent intervention capabilities. In this paper, we propose the Causal Graph Routing (CGR) framework, an integrated causal scheme relying entirely on the intervention mechanisms to reveal the cause-effect forces hidden in data. Specifically, CGR is composed of a stack of causal layers. Each layer includes a set of parallel deconfounding blocks from different causal graphs. We combine these blocks via the concept of the proposed sufficient cause, which allows the model to dynamically select the suitable deconfounding methods in each layer. CGR is implemented as the stacked networks, integrating no confounder, back-door adjustment, front-door adjustment, and probability of sufficient cause. We evaluate this framework on two classical tasks of CV and NLP. Experiments show CGR can surpass the current state-of-the-art methods on both Visual Question Answer and Long Document Classification tasks. In particular, CGR has great potential in building the "causal" pre-training large-scale model that effectively generalizes to diverse tasks. It will improve the machines' comprehension of causal relationships within a broader semantic space.

Tianxing Wu, Shutong Zhu, Jingting Wang et al.

Uncertain knowledge graphs (UKGs) associate each triple with a confidence score to provide more precise knowledge representations. Recently, since real-world UKGs suffer from the incompleteness, uncertain knowledge graph (UKG) completion attracts more attention, aiming to complete missing triples and confidences. Current studies attempt to learn UKG embeddings to solve this problem, but they neglect the extremely imbalanced distributions of triple confidences. This causes that the learnt embeddings are insufficient to high-quality UKG completion. Thus, in this paper, to address the above issue, we propose a new semi-supervised Confidence Distribution Learning (ssCDL) method for UKG completion, where each triple confidence is transformed into a confidence distribution to introduce more supervision information of different confidences to reinforce the embedding learning process. ssCDL iteratively learns UKG embedding by relational learning on labeled data (i.e., existing triples with confidences) and unlabeled data with pseudo labels (i.e., unseen triples with the generated confidences), which are predicted by meta-learning to augment the training data and rebalance the distribution of triple confidences. Experiments on two UKG datasets demonstrate that ssCDL consistently outperforms state-of-the-art baselines in different evaluation metrics.