Cross-lingual word alignment is the task for word translation between monolingual word embedding spaces of two different languages. Recent work is mostly based on supervised approaches, while their success relies on bilingual seed dictionaries derived from aligned data. The unsupervised adversarial approaches, which utilize generative adversarial networks (GANs) to map the global monolingual space to another space, can eliminate the need for aligned data. However, most GAN-based unsupervised approaches ignore the issues of mode collapse and gradient disappearance in GANs, leading to a training failure to converge. In addition, these approaches often fail to account for the low isomorphism between language pairs, which prevents capturing the non-linear relationship contained in cross-lingual embedding spaces. To address these issues, we propose a novel unsupervised unified framework with an adaptive training objective for the GANs' improvement (ATOGAN) and a local mapping (LM) strategy for exploring the non-linear relationship. We present ATOGAN to learn bi-directional global mapping using unaligned word embeddings, which integrates particle swarm optimization (PSO) to adaptively select the training objective for preventing mode collapse and gradient disappearance. Then, we design an LM strategy based on the guidance of dictionaries generated by trained ATOGAN to alleviate reliance on isomorphism assumption for purely linear mapping. Experimental results demonstrate the effectiveness of our proposed method for cross-lingual word alignment in low isomorphic embedding spaces (distant language pairs). Our code is available athttps://github.com/goFurtherLong/ATOGAN.

In the modern supply chain system, large-scale transportation tasks require the collaborative work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle route planning was mainly implemented by heuristic algorithms. However, these algorithms face the dilemma of long computation time. In recent years, some machine learning-based methods are also proposed for vehicle route planning, but the existing algorithms can hardly solve multi-vehicle time-sensitive problems. To overcome this problem, we propose a novel multi-agent reinforcement learning model, which optimizes the route length and the vehicle’s arrival time simultaneously. The model is based on the encoder-decoder framework. The encoder mines the relationship between the customer nodes in the problem, and the decoder generates the route of each vehicle iteratively. Specially, we design multiple route recorders to extract the route history information of vehicles and realize the communication between them. In the inferring phase, the model could immediately generate routes for all vehicles in a new instance. To further improve the performance of the model, we devise a multi-sampling strategy and obtain the balance boundary between computation time and performance improvement. In addition, we propose a simulation-based vehicle configuration method to select the optimal number of vehicles in real applications. For validation, we conduct a series of experiments on problems with different customer amounts and various vehicle numbers. The results show that the proposed model outperforms other typical algorithms in both performance and calculation time.