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The Counter Narrative (CN) is a promising approach to combat online hate speech (HS) without infringing on freedom of speech. In recent years, there has been a growing interest in automatically generating CNs using natural language generation techniques. However, current automatic CN generation methods mainly rely on expert-authored datasets for training, which are time-consuming and labor-intensive to acquire. Furthermore, these methods cannot directly obtain and extend counter-knowledge from external statistics, facts, or examples. To address these limitations, we propose Retrieval-Augmented Unsupervised Counter Narrative Generation (RAUCG) to automatically expand external counter-knowledge and map it into CNs in an unsupervised paradigm. Specifically, we first introduce an SSF retrieval method to retrieve counter-knowledge from the multiple perspectives of stance consistency, semantic overlap rate, and fitness for HS. Then we design an energy-based decoding mechanism by quantizing knowledge injection, countering and fluency constraints into differentiable functions, to enable the model to build mappings from counter-knowledge to CNs without expert-authored CN data. Lastly, we comprehensively evaluate model performance in terms of language quality, toxicity, persuasiveness, relevance, and success rate of countering HS, etc. Experimental results show that RAUCG outperforms strong baselines on all metrics and exhibits stronger generalization capabilities, achieving significant improvements of +2.0% in relevance and +4.5% in success rate of countering metrics. Moreover, RAUCG enabled GPT2 to outperform T0 in all metrics, despite the latter being approximately eight times larger than the former. Warning: This paper may contain offensive or upsetting content!

Interlayer link prediction aims at matching the same entities across different layers of the multiplex network. Existing studies attempt to predict more accurately, efficiently, or generically from the aspects of network structure, attribute characteristics, and their combination. Few of them analyze the effects of intralayer links. Namely, few works study the backbone structures which can effectively preserve the predictive accuracy while dealing with a smaller number of intralayer links. It can be used to investigate what types of intralayer links are most important for correct prediction. Are there any intralayer links whose presence leads to worse predictive performance than their absence, and how to attack the prediction algorithms at the minimum cost? To this end, two kinds of network structural perturbation methods are proposed. For the scenario where the structural information of the whole network is completely known, we offer a global perturbation strategy that gives different perturbation weights to different types of intralayer links and then selects a predetermined proportion of intralayer links to remove according to the weights. In contrast, if these information cannot be obtained at one time, we design a biased random walk procedure, local perturbation strategy, to execute perturbation. Four kinds of interlayer link prediction algorithms are carried out on different real-world and artificial perturbed multiplex networks. We find out that the intralayer links connected with small degree nodes have the most significant impact on the prediction accuracy. The intralayer links connected with large degree nodes may have side effects on the interlayer link prediction.

Cross-Lingual Summarization (CLS) is the task to generate a summary in one language for an article in a different language. Previous studies on CLS mainly take pipeline methods or train the end-to-end model using the translated parallel data. However, the quality of generated cross-lingual summaries needs more further efforts to improve, and the model performance has never been evaluated on the hand-written CLS dataset. Therefore, we first propose a clue-guided cross-lingual abstractive summarization method to improve the quality of cross-lingual summaries, and then construct a novel hand-written CLS dataset for evaluation. Specifically, we extract keywords, named entities, etc. of the input article as key clues for summarization and then design a clue-guided algorithm to transform an article into a graph with less noisy sentences. One Graph encoder is built to learn sentence semantics and article structures and one Clue encoder is built to encode and translate key clues, ensuring the information of important parts are reserved in the generated summary. These two encoders are connected by one decoder to directly learn cross-lingual semantics. Experimental results show that our method has stronger robustness for longer inputs and substantially improves the performance over the strong baseline, achieving an improvement of 8.55 ROUGE-1 (English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English summarization) scores over the existing SOTA., Comment: 12 pages,4 figures