The integration of multi-modal information, especially the graphic features of Hanzi, is crucial for improving the performance of Chinese Named Entity Recognition (NER) tasks. However, existing glyph-based models frequently neglect the relationship between pictorial elements and radicals. This paper presents AHVE-CNER, a model that integrates multi-source visual and phonetic information of Hanzi, while explicitly aligning pictographic features with their corresponding radicals. We propose the Gated Pangu-𝜋 Cross Transformer to effectively facilitate the integration of these multi-modal representations. By leveraging a multi-source glyph alignment strategy, AHVE-CNER demonstrates an improved capability to capture the visual and semantic nuances of Hanzi for NER tasks. Extensive experiments on benchmark datasets validate that AHVE-CNER achieves superior performance compared to existing multi-modal Chinese NER methods. Additional ablation studies further confirm the effectiveness of our visual alignment module and the fusion approach.

Multiparty dialogue question answering (QA) in machine reading comprehension (MRC) is a challenging task due to its complex information flow interactions and logical QA inference. Existing models typically handle such QA tasks by decoupling dialogue information at both speaker and utterance levels. However, few of them consider the logical inference relations in multiparty dialogue QA, leading to suboptimal QA performance. To address this issue, this paper proposes a memory network with logical inference (LIMN) for extractive QA in multiparty dialogues. LIMN introduces an inference module, which is pretrained by incorporating plain QA articles as external knowledge. It generates logical inference-aware representations from latent space for multiparty dialogues. To further model complex interactions among logical dialogue contexts, questions and key-utterance information, a key-utterance-based interaction method is proposed for leverage. Moreover, a multitask learning strategy is adopted for robust MRC. Extensive experiments were conducted on Molweni and FriendsQA benchmarks, which included 25k and 10k questions, respectively. Comparative results showed that LIMN achieves state-of-the-art results on both benchmarks, demonstrating the enhancement of logical QA inference in multiparty dialogue QA tasks.