This paper explores a novel lightweight approach LightFair to achieve fair text-to-image diffusion models (T2I DMs) by addressing the adverse effects of the text encoder. Most existing methods either couple different parts of the diffusion model for full-parameter training or rely on auxiliary networks for correction. They incur heavy training or sampling burden and unsatisfactory performance. Since T2I DMs consist of multiple components, with the text encoder being the most fine-tunable and front-end module, this paper focuses on mitigating bias by fine-tuning text embeddings. To validate feasibility, we observe that the text encoder’s neutral embedding output shows substantial skewness across image embeddings of various attributes in the CLIP space. More importantly, the noise prediction network further amplifies this imbalance. To finetune the text embedding, we propose a collaborative distance-constrained debiasing strategy that balances embedding distances to improve fairness without auxiliary references. However, mitigating bias can compromise the original generation quality. To address this, we introduce a two-stage text-guided sampling strategy to limit when the debiased text encoder intervenes. Extensive experiments demonstrate that LightFair is effective and efficient. Notably, on Stable Diffusion v1.5, our method achieves SOTA debiasing at just $1/4$ of the training burden, with virtually no increase in sampling burden. The code is available at https://github.com/boyuh/LightFair.

Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English.However, improvements on Chinese SC task are still quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses.Furthermore, entire neural Chinese SC models have been under-investigated so far.In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. 1 Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on the Chinese SC task.

Entity Linking (EL) is the task of mapping mentions in texts to the corresponding entities in knowledge bases. Existing studies mostly focus on joint disambiguation based on the topical coherence, including graph and sequence models. Sequence models alleviate the complexity caused by graph models, but exist the error propagation that incorrectly disambiguated entities are likely to induce further errors when predicting future mentions. Moreover, it is a huge expense to construct the relationship between entities to explore structured knowledge. To address these problems, we propose a novel method, Knowledge Enhanced Sequential Entity Linking (KESEL), which converts global EL into a sequence decision problem and applies a pre-trained language model to better fuse entity knowledge. Specifically, we firstly utilize multiple features to learn local contextual representations of mentions and candidates respectively. Next, a sequential ERNIE model is introduced to generate knowledgeable representations by dynamically integrating the knowledge of previously referred entities into subsequent mentions disambiguation. Finally, by concatenating the above learned contextual and knowledgeable representations, we make full use of multi-semantic information to improve the performance of EL. Extensive experiments show that our method can achieve competitive or state-of-the-art results.