Automated analytical techniques for magnetocardiography (MCG) are essential for diagnosing and predicting cardiovascular diseases. Clinically acquired MCG signals are often contaminated by various types of noise, which negatively impact subsequent signal analysis. However, traditional methods have limitations in denoising long-term MCG signals with complex spatial structures. We propose a high-precision, robust representation learning method based on skip connection multi-scale feature fusion (SkipDAEformer) for effectively removing random mixed noise in MCG signals. SkipDAEformer integrates attention fusion mechanisms into a basic denoising autoencoder to extract and fuse critical temporal and spatial information from each feature map, thus enhancing the model's ability to capture long-range dependencies and spatial features in MCG signals. Meanwhile, we further supplement and refine the semantic information for the feature maps through a global feature fusion method. By fusing multi-scale features from different skip connections, SkipDAEformer can learn more comprehensive representations of MCG signals, enabling the effective separation of clean signals from noise. Experimental results demonstrate that SkipDAEformer outperforms existing methods in denoising performance, channel consistency, feature consistency, and generalization ability and can be extended to a self-supervised learning framework. In actual noise reduction and diagnostic classification tasks, SkipDAEformer shows superior clinical acceptability and diagnostic value, potentially advancing MCG data analysis.

With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important.Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowledge.Meanwhile, as the neural networkbased (NN-based) methods develop, NNbased KB-QA has already achieved impressive results.However, previous work did not put more emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers.This simple representation strategy is not easy to express the proper information in the question.Hence, we present an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism.In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers.As a result, it could alleviates the out-of-vocabulary (OOV) problem, which helps the crossattention model to represent the question more precisely.The experimental results on WebQuestions demonstrate the effectiveness of the proposed approach.

Zhenghua Li, Min Zhang, Yue Zhang, Zhanyi Liu, Wenliang Chen, Hua Wu, Haifeng Wang. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact that the words in a collocation tend to co-occur in similar contexts as in bilingual word alignment. First, the monolingual corpus is replicated to generate a parallel corpus, in which each sentence pair consists of two identical sentences. Next, the monolingual word alignment algorithm is employed to align potentially collocated words. Finally, the aligned word pairs are ranked according to the alignment scores and candidates with higher scores are extracted as collocations. We conducted experiments on Chinese and English corpora respectively. Compared to previous approaches that use association measures to extract collocations from co-occurrence word pairs within a given window, our method achieves higher precision and recall. According to human evaluation, our method achieves precisions of 62% on a Chinese corpus and 64% on an English corpus. In particular, we can extract collocations with longer spans, achieving a higher precision of 83% on the long-span (> 6 words) Chinese collocations.

This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.

This paper proposes a novel Example-Based Machine Translation (EBMT) method based on Tree String Correspondence (TSC) and statistical generation. In this method, the translation examples are represented as TSC, which consists of three parts: a parse tree in the source language, a string in the target language, and the correspondences between the leaf nodes of the source language tree and the substrings of the target language string. During the translation, the input sentence is first parsed into a tree. Then the TSC forest is searched out if it is best matched with the parse tree. The translation is generated by using a statistical generation model to combine the target language strings in the TSCs. The generation model consists of three parts: the semantic similarity between words, the word translation probability, and the target language model. Based on the above method, we build an English-to-Chinese Machine Translation (ECMT) system. Experimental results indicate that the performance of our system is comparable with that of the state-of-the-art commercial ECMT systems.