Short-video platforms now host vast multimodal ads whose deceptive visuals, speech and subtitles demand finer-grained, policy-driven moderation than community safety filters. We present BLM-Guard, a content-audit framework for commercial ads that fuses Chain-of-Thought reasoning with rule-based policy principles and a critic-guided reward. A rule-driven ICoT data-synthesis pipeline jump-starts training by generating structured scene descriptions, reasoning chains and labels, cutting annotation costs. Reinforcement learning then refines the model using a composite reward balancing causal coherence with policy adherence. A multitask architecture models intra-modal manipulations (e.g., exaggerated imagery) and cross-modal mismatches (e.g., subtitle–speech drift), boosting robustness. Experiments on real short-video ads show BLM-Guard surpasses strong baselines in accuracy, consistency and generalization.

Source code representation learning is the basis of applying artificial intelligence to many software engineering tasks such as code clone detection, algorithm classification, and code summarization. Recently, many works have tried to improve the performance of source code representation from various perspectives, e.g., introducing the structural information of programs into latent representation. However, when dealing with rapidly expanded unlabeled cross-language source code datasets from the Internet, there are still two issues. Firstly, deep learning models for many code-specific tasks still suffer from the lack of high-quality labels. Secondly, the structural differences among programming languages make it more difficult to process multiple languages in a single neural architecture. To address these issues, in this article, we propose a novel Cross -language Code representation with a large-scale pre-training ( XCode ) method. Concretely, we propose to use several abstract syntax trees and ELMo-enhanced variational autoencoders to obtain multiple pre-trained source code language models trained on about 1.5 million code snippets. To fully utilize the knowledge across programming languages, we further propose a Shared Encoder-Decoder (SED) architecture which uses the multi-teacher single-student method to transfer knowledge from the aforementioned pre-trained models to the distilled SED. The pre-trained models and SED will cooperate to better represent the source code. For evaluation, we examine our approach on three typical downstream cross-language tasks, i.e., source code translation, code clone detection, and code-to-code search, on a real-world dataset composed of programming exercises with multiple solutions. Experimental results demonstrate the effectiveness of our proposed approach on cross-language code representations. Meanwhile, our approach performs significantly better than several code representation baselines on different downstream tasks in terms of multiple automatic evaluation metrics.

Qianglong Chen, Feng Ji, Xiangji Zeng, Feng-Lin Li, Ji Zhang, Haiqing Chen, Yin Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Past progress on neural models has proven that named entity recognition is no longer a problem if we have enough labeled data.However, collecting enough data and annotating them are labor-intensive, time-consuming, and expensive.In this paper, we decompose the sentence into two parts: entity and context, and rethink the relationship between them and model performance from a causal perspective.Based on this, we propose the Counterfactual Generator, which generates counterfactual examples by the interventions on the existing observational examples to enhance the original dataset.Experiments across three datasets show that our method improves the generalization ability of models under limited observational examples.Besides, we provide a theoretical foundation by using a structural causal model to explore the spurious correlations between input features and output labels.We investigate the causal effects of entity or context on model performance under both conditions: the non-augmented and the augmented.Interestingly, we find that the non-spurious correlations are more located in entity representation rather than context representation.As a result, our method eliminates part of the spurious correlations between context representation and output labels.The code is available at https://github.com/xijiz/cfgen.