Yuhang Niu, Hongyuan Xu, Ciyi Liu, Bofan Wei, Jiaqi Ye, Yanlong Wen, Xiaojie Yuan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Multivariate time series forecasting has become increasingly crucial in fields such as energy and transportation. Recent research has focused on local lag relationships across variates, yielding impressive results. However, these methods typically require pre-calculating lag indicators and steps between variates based on historical data. This reliance on pre-calculated lag steps neglects the potential variability in lag steps over the historical and predicted time series. In this paper, we propose LagTS, a novel method that adaptively models lag relationships across variates in multivariate time series data and effectively leverages these relationships to improve prediction accuracy. Specifically, the proposed method extracts the lag relationships using a dedicated module. Moreover, it employs a lag relation loss to facilitate the adaptive modeling of the lag relationships. Extensive experimental results on four publicly available datasets and an industry dataset demonstrate the effectiveness of the proposed method.

Abstract Low-resource taxonomy completion aims to automatically insert new concepts into the existing taxonomy, in which only a few in-domain training samples are available. Recent studies have achieved considerable progress by incorporating prior knowledge from pre-trained language models (PLMs). However, these studies tend to overly rely on such knowledge and neglect the shareable knowledge across different taxonomies. In this paper, we propose TaxoPro, a plug-in LoRA-based cross-domain method, that captures shareable knowledge from the high- resource taxonomy to improve PLM-based low-resource taxonomy completion techniques. To prevent negative interference between domain-specific and domain-shared knowledge, TaxoPro decomposes cross- domain knowledge into domain-shared and domain-specific components, storing them using low-rank matrices (LoRA). Additionally, TaxoPro employs two auxiliary losses to regulate the flow of shareable knowledge. Experimental results demonstrate that TaxoPro improves PLM-based techniques, achieving state-of-the-art performance in completing low-resource taxonomies. Code is available at https://github.com/cyclexu/TaxoPro.

Automatic taxonomy completion aims to attach the emerging concept to an appropriate pair of hypernym and hyponym in the existing taxonomy.Existing methods suffer from the overfitting to leaf-only problem caused by imbalanced leaf and non-leaf samples when training the newly initialized classification head.Besides, they only leverage subtasks, namely attaching the concept to its hypernym or hyponym, as auxiliary supervision for representation learning yet neglect the effects of subtask results on the final prediction.To address the aforementioned limitations, we propose TacoPrompt, a Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion.First, we perform triplet semantic matching using the prompt learning paradigm to effectively learn non-leaf attachment ability from imbalanced training samples.Second, we design the result context to relate the final prediction to the subtask results by a contextual approach, enhancing prompt-based multi-task learning.Third, we leverage a two-stage retrieval and re-ranking approach to improve the inference efficiency.Experimental results on three datasets show that TacoPrompt achieves state-of-the-art taxonomy completion performance.Codes are available at https://github.com/cyclexu/TacoPrompt.