Yilin Shen, Xiangyu Zeng, Hongxia Jin. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Water resources in arid and semi-arid inland regions are highly vulnerable, facing threats from global climate change and human activities. Ensuring water resource sustainability requires scientifically evaluating the vulnerability of water resources and its driving factors. Taking the Shule River Basin, an inland river in northwest China, as an example, this study established an assessment system considering 16 influencing factors based on three aspects, including natural vulnerability, anthropogenic vulnerability, and carrying capacity vulnerability. The mature-element fuzzy model based on comprehensive weight and the Delphi method were used to evaluate the water resource vulnerability of the basin from 2005 to 2021. The results were as follows: (1) The water resource vulnerability in the Shule River Basin was between severe and moderate from 2005 to 2021, with a trend towards severe vulnerability. (2) The barrier analysis at the index level shows that factors of natural vulnerability had a low impact on water resource vulnerability in the basin from 2005 to 2019 but exerted a greater impact in 2020–2021, an impact that was far higher than that caused by factors of man-made vulnerability and water resource vulnerability. The impact of factors of anthropogenic vulnerability on water resource vulnerability was relatively low in the entire study period, except in 2016, 2017, 2020, and 2021. In 2005–2010, the factors of bearing capacity vulnerability had a great impact on water resource vulnerability, but in 2011–2021, the impact was alleviated and was gradually reduced. (3) Among the 16 factors affecting water resource vulnerability, the obstacle degree was higher than 6.5% for the following factors: the ratio of irrigation water use on farmland, the annual precipitation, total water resources, the annual quantity of wastewater effluent, the urbanization rate, the surface water control rate, and the degree of groundwater resource amount, exploration, and utilization obstacle values.

Shangqian Gao, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e.g., by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user's relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.