Lusha Wang

Lusha Wang, Yuchen Li, Su Yuan et al.

Chinese word segmentation is especially fragile in non-standard text, where language learner errors and other character-level divergences disrupt the word boundaries assumed by downstream annotation and evaluation. This paper formulates Chinese word boundary recovery as an alignment-based projection task. Given a noisy source sentence and a cleaner target counterpart, we first align the two strings at the character level and then project target-side word boundaries back onto the source. Beyond the recovery method itself, we introduce two evaluation resources: a manually checked learner Chinese benchmark based on MuCGEC and a controlled synthetic benchmark derived from the Chinese Penn Treebank. Experiments show that direct segmentation remains vulnerable to compound fragmentation in learner input, whereas the proposed two step projection method corrects many over-segmentation errors by using the corrected target to recover source-side word spans. The results show that word boundary recovery is distinct from ordinary segmentation and that alignment projection provides a principled mechanism for stabilizing Chinese annotation and evaluation under noisy input.

Ravi Raj Shrestha, Zhi Zhou, Limon Barua et al.

The anticipated widespread adoption of electric vehicles (EVs) necessitates a critical evaluation of existing power distribution infrastructures, as EV integration imposes additional stress on distribution networks that can lead to component overloading and power quality degradation. Implementing smart charging mechanisms can mitigate these adverse effects and defer or even avoid upgrades. This study assesses the performance of two smart charging strategies - Time of Use (TOU) pricing and Load Balancing (LB) on seven representative real-world feeders identified using k-means clustering. A time series-based steady-state load flow analysis was conducted on these feeders to simulate the impact of EV charging under both strategies across four different EV enrollment scenarios and three representative days to capture seasonal load characteristics. A grid upgrade strategy has been proposed to strengthen the power grid to support EV integration with minimal cost. Results demonstrate that both TOU and LB strategies effectively manage the additional EV load with reduced upgrade requirement and cost to existing infrastructure compared to the case without smart charging strategies and LB outperforms TOU when the customer enrollment levels are high. These findings support the viability of smart charging in facilitating EV integration while maintaining distribution network reliability and reducing investment cost.

Nicole Lai-Lopez, Lusha Wang, Su Yuan et al.

In this work, we introduce our solution for the Multilingual Text Detoxification Task in the PAN-2025 competition for the ylmmcl team: a robust multilingual text detoxification pipeline that integrates lexicon-guided tagging, a fine-tuned sequence-to-sequence model (s-nlp/mt0-xl-detox-orpo) and an iterative classifier-based gatekeeping mechanism. Our approach departs from prior unsupervised or monolingual pipelines by leveraging explicit toxic word annotation via the multilingual_toxic_lexicon to guide detoxification with greater precision and cross-lingual generalization. Our final model achieves the highest STA (0.922) from our previous attempts, and an average official J score of 0.612 for toxic inputs in both the development and test sets. It also achieved xCOMET scores of 0.793 (dev) and 0.787 (test). This performance outperforms baseline and backtranslation methods across multiple languages, and shows strong generalization in high-resource settings (English, Russian, French). Despite some trade-offs in SIM, the model demonstrates consistent improvements in detoxification strength. In the competition, our team achieved ninth place with a score of 0.612.