Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

A Grid-Based Method to Represent the Covariance Structure for Earthquake Ground Motion

Spatial variation of earthquake ground motion is an important phenomenon that cannot be ignored in the design and safety of strategic structures. However, almost all the procedures for the evaluation of variation assumed that the random field is homogeneous in space. It is obvious that reality does not fully conform to the assumption. How to investigate the inhomogeneous feature of ground motion in space is a challenge for researcher. A body-fitted grid-coordinates-based method is proposed to estimate and describe the local spatial variations for the earthquake ground motion; it need not to make the assumption that the random field of earthquake is homogeneous in space. An analysis of spatial variability of seismic motion in smart-1 array monitored in Lotung, Taiwan demonstrates this methodology

Dynamic Time Warping Distance Method for Similarity Test of Multipoint Ground Motion Field

The reasonability of artificial multi-point ground motions and the identification of abnormal records in seismic array observations, are two important issues in application and analysis of multi-point ground motion fields. Based on the dynamic time warping (DTW) distance method, this paper discusses the application of similarity measurement in the similarity analysis of simulated multi-point ground motions and the actual seismic array records. Analysis results show that the DTW distance method not only can quantitatively reflect the similarity of simulated ground motion field, but also offers advantages in clustering analysis and singularity recognition of actual multi-point ground motion field

Additional file 1 of Diagnostic biomarkers and aortic dissection: a systematic review and meta-analysis

Supplementary Material