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Counter-argument generation -- a captivating area in computational linguistics -- seeks to craft statements that offer opposing views. While most research has ventured into paragraph-level generation, sentence-level counter-argument generation beckons with its unique constraints and brevity-focused challenges. Furthermore, the diverse nature of counter-arguments poses challenges for evaluating model performance solely based on n-gram-based metrics. In this paper, we present the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum. We also propose Arg-LlaMA for generating high-quality counter-argument. For better evaluation, we trained a BERT-based evaluator Arg-Judge with human preference data. We conducted comparative experiments involving various baselines such as LlaMA, Alpaca, GPT-3, and others. The results show the competitiveness of our proposed framework and evaluator in counter-argument generation tasks. Code and data are available at https://github.com/amazingljy1206/ArgTersely., Comment: EMNLP2023, main conference

In order to be applicable in real-world scenario, Boundary Attacks (BAs) were proposed and ensured one hundred percent attack success rate with only decision information. However, existing BA methods craft adversarial examples by leveraging a simple random sampling (SRS) to estimate the gradient, consuming a large number of model queries. To overcome the drawback of SRS, this paper proposes a Latin Hypercube Sampling based Boundary Attack (LHS-BA) to save query budget. Compared with SRS, LHS has better uniformity under the same limited number of random samples. Therefore, the average on these random samples is closer to the true gradient than that estimated by SRS. Various experiments are conducted on benchmark datasets including MNIST, CIFAR, and ImageNet-1K. Experimental results demonstrate the superiority of the proposed LHS-BA over the state-of-the-art BA methods in terms of query efficiency. The source codes are publicly available at https://github.com/GZHU-DVL/LHS-BA.

Image inpainting has achieved great advances by simultaneously leveraging image structure and texture features. However, due to lack of effective multi-feature fusion techniques, existing image inpainting methods still show limited improvement. In this paper, we design a deep multi-feature co-learning network for image inpainting, which includes Soft-gating Dual Feature Fusion (SDFF) and Bilateral Propagation Feature Aggregation (BPFA) modules. To be specific, we first use two branches to learn structure features and texture features separately. Then the proposed SDFF module integrates structure features into texture features, and meanwhile uses texture features as an auxiliary in generating structure features. Such a co-learning strategy makes the structure and texture features more consistent. Next, the proposed BPFA module enhances the connection from local feature to overall consistency by co-learning contextual attention, channel-wise information and feature space, which can further refine the generated structures and textures. Finally, extensive experiments are performed on benchmark datasets, including CelebA, Places2, and Paris StreetView. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art. The source codes are available at https://github.com/GZHU-DVL/MFCL-Inpainting.

This study investigates the influence of the risk factor (equity) and environmental conditions have on the cost efficiency of three developing Asian countries, China mainland, India and Indonesia for cross-country comparisons of efficiency using a parametric approach, covering period of year 2006 to 2012. The goal of this analysis is to identify the impacts brought by equity and environmental variables on commercial banking efficiency in China mainland, India and Indonesia, especially the 2008 global financial crisis. To conduct this analysis, stochastic frontier analysis is utilized. Results show that the environmental conditions of each country play important roles in the definition and specification of the common frontier of different countries. Results suggest that the banking efficiency in China mainland, India and Indonesia has a gradually declined tendency before and after incorporating the environmental variables. Without the environmental variables, the average cost-efficiency scores of three countries line in the range of 83.6% to 83.9% from year 1999 to year 2006 and in the range of 77.6% to 78.6% for the period of 2007 to 2012. When environmental variables are included in the model, the differences between two periods are increased substantially, ranging from 80% to 45%. After incorporating the risk factor (equity), cost efficiency scores for commercial banking sector in China mainland, India and Indonesia have become slightly lowered, but show a same tendency as the ones when the risk factor is removed. Overall, the results demonstrate that environmental variables contribute significantly to the difference in efficiency scores between the three counties. In other words, failure to incorporate the environmental factors may not be able to adequately describe the banking efficiency tendency of China mainland, India and Indonesia in the period of 1999 to 2012. However, the results also indicate that the impact of equity makes slightly difference on the leve

This study investigates the influence of the risk factor (equity) and environmental conditions have on the cost efficiency of three developing Asian countries, China mainland, India and Indonesia for cross-country comparisons of efficiency using a parametric approach, covering period of year 2006 to 2012. The goal of this analysis is to identify the impacts brought by equity and environmental variables on commercial banking efficiency in China mainland, India and Indonesia, especially the 2008 global financial crisis. To conduct this analysis, stochastic frontier analysis is utilized. Results show that the environmental conditions of each country play important roles in the definition and specification of the common frontier of different countries. Results suggest that the banking efficiency in China mainland, India and Indonesia has a gradually declined tendency before and after incorporating the environmental variables. Without the environmental variables, the average cost-efficiency scores of three countries line in the range of 83.6% to 83.9% from year 1999 to year 2006 and in the range of 77.6% to 78.6% for the period of 2007 to 2012. When environmental variables are included in the model, the differences between two periods are increased substantially, ranging from 80% to 45%. After incorporating the risk factor (equity), cost efficiency scores for commercial banking sector in China mainland, India and Indonesia have become slightly lowered, but show a same tendency as the ones when the risk factor is removed. Overall, the results demonstrate that environmental variables contribute significantly to the difference in efficiency scores between the three counties. In other words, failure to incorporate the environmental factors may not be able to adequately describe the banking efficiency tendency of China mainland, India and Indonesia in the period of 1999 to 2012. However, the results also indicate that the impact of equity makes slightly difference on the leve

This study investigates the influence of the risk factor (equity) and environmental conditions have on the cost efficiency of three developing Asian countries, China mainland, India and Indonesia for cross-country comparisons of efficiency using a parametric approach, covering period of year 2006 to 2012. The goal of this analysis is to identify the impacts brought by equity and environmental variables on commercial banking efficiency in China mainland, India and Indonesia, especially the 2008 global financial crisis. To conduct this analysis, stochastic frontier analysis is utilized. Results show that the environmental conditions of each country play important roles in the definition and specification of the common frontier of different countries. Results suggest that the banking efficiency in China mainland, India and Indonesia has a gradually declined tendency before and after incorporating the environmental variables. Without the environmental variables, the average cost-efficiency scores of three countries line in the range of 83.6% to 83.9% from year 1999 to year 2006 and in the range of 77.6% to 78.6% for the period of 2007 to 2012. When environmental variables are included in the model, the differences between two periods are increased substantially, ranging from 80% to 45%. After incorporating the risk factor (equity), cost efficiency scores for commercial banking sector in China mainland, India and Indonesia have become slightly lowered, but show a same tendency as the ones when the risk factor is removed. Overall, the results demonstrate that environmental variables contribute significantly to the difference in efficiency scores between the three counties. In other words, failure to incorporate the environmental factors may not be able to adequately describe the banking efficiency tendency of China mainland, India and Indonesia in the period of 1999 to 2012. However, the results also indicate that the impact of equity makes slightly difference on the leve