The semantic matching problem detects whether the candidate text is related to a specific input text. Basic text matching adopts the method of statistical vocabulary information without considering semantic relevance. Methods based on Convolutional neural networks (CNN) and Recurrent networks (RNN) provide a more optimized structure that can merge the information in the entire sentence into a single sentence-level representation. However, these representations are often not suitable for sentence interactive learning. We design a multi-dimensional semantic interactive learning model based on the mechanism of multiple written heads in the transformer architecture, which not only considers the correlation and position information between different word levels but also further maps the representation of the sentence to the interactive three-dimensional space, so as to solve the problem and the answer can select the best word-level matching pair, respectively. Experimentally, the algorithm in this paper was tested on Yahoo! and StackEx open-domain datasets. The results show that the performance of the method proposed in this paper is superior to the previous CNN/RNN and BERT-based methods.

Recent advancements in network and multimedia technologies have facilitated the distribution and sharing of digital videos over the Internet. These long videos contain very complex contents. Additionally, it is very challenging to use as few frames as possible to cover the video contents without missing too much information. There are at least two ways to describe these complex videos contents with minimal frames: the keyframes extracted from the video or the video summary. The former lays stress on covering the whole video contents as much as possible. The latter emphasizes covering the video contents of interest. As a consequence, keyframes are widely used in many areas such as video segmentation and object tracking. In this paper, we propose a keyframe extraction method based on multiple features via a novel combinatorial optimization algorithm. The key frame extraction is modeled as a combinatorial optimization problem. A fast dynamic programming algorithm based on a forward non-overlapping transfer matrix in polynomial time and a 0-1 integer linear programming algorithm based on an overlapping matrix is proposed to solve our maximization problem. In order to quantitatively evaluate our approach, a long video dataset named 'Animal world' is self-constructed, and the segmentation evaluation criterions are introduced. A good result is achieved on 'Animal world' dataset and a public available Keyframe-Sydney KFSYD dataset ^[1].