Human-generated passwords are naturally rich in structure, making them vulnerable to guessing attacks. Recently, people have employed strong deep neural networks to model and generate human-generated passwords, showing its great potential for efficient password guessing. However, the research on password guessing decoding algorithms, which is the other essential part of the sequence generation task, remains barely explored. In this paper, we provide an in-depth analysis of the two most widely used classes of sequence decoding algorithms, i.e., Beam Search and Sampling, and find out that a dynamically adjusted Temperature Sampling could be the most suitable one for massive password generation. However, it takes a lot of knowledge and practice even for a human expert to control the temperature parameters appropriately. Thus, we provide a Reinforcement learning-based Dynamic Temperature Sampling algorithm for massive password generation. We first train a Transformer-based password language model in an auto-regressive fashion. Next, we generate the passwords using Temperature Sampling in a batched manner. A neural Q-network is trained to adjust the temperature parameter automatically for each generation batch. A lower temperature value at the start batches allows the most common passwords to be generated rapidly. Then the temperature is gradually increased to generate more non-repetitive long-tail passwords. Experimental results demonstrate that our proposed method far outperforms baseline methods in terms of both generation speed and hit rate.

Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of datasets. Recent studies are primarily based on pseudo-cross-lingual datasets obtained by translation. Such an approach would inevitably lead to the loss of information in the original document and introduce noise into the summary, thus hurting the overall performance. In this paper, we present CATAMARAN, the first high-quality cross-lingual long text abstractive summarization dataset. It contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average lengths of articles are 1133.65 for English articles and 2035.33 for Chinese articles, and the average lengths of the summaries are 26.59 and 70.05, respectively. We train and evaluate an mBART-based cross-lingual abstractive summarization model using our dataset. The result shows that, compared with mono-lingual systems, the cross-lingual abstractive summarization system could also achieve solid performance.

The release of BERT revolutionized the development of NLP.Various BERT-based reading comprehension models have been proposed, thus updating the performance ranking of reading comprehension tasks.However, the above BERT-based models inherently employ BERT's combined input method, representing the input question and paragraph as a single packed sequence, without further modification for reading comprehension.This paper makes an in-depth analysis of this input method, proposes a problem of this approach.We call it attention deconcentration.Accordingly, this paper proposes ForceReader, a BERT-based interactive machine reading comprehension model.First, ForceReader proposes a novel solution called the Attention Separation Representation to respond to attention deconcentration.Moreover, starting from the logical nature of reading comprehension tasks, ForceReader adopts Multi-mode Reading, and Interactive Reasoning strategy.For the calculation of attention, ForceReader employs Conditional Background Attention to solve the lack of the overall context semantic after the separation of attention.As an integral model, ForceReader shows a significant improvement in reading comprehension tasks compared to BERT.Moreover, this paper makes detailed visual analyses of the attention and propose strategies accordingly.This may be another argument to the explanations of the attention.