Named entity recognition (NER) extracts entity spans and assigns them to predefined types. Large language models (LLMs) are increasingly used for NER, but most LLM-based methods still rely on a mainly semantic view, judging whether a candidate span is compatible with an entity type. Although effective, this view can miss entities with weak type semantics, over-accept semantically salient modifiers, and leave little chance to recover spans omitted by early candidate extraction. We argue that entity spans are also traceable in sentence form: local structure and prepositions provide cues about a span’s position and function. We propose SSDA-NER, a Semantic-Structural Dual-path Arbitration framework that combines semantic understanding with structural analysis. Specifically, the Semantic path checks candidate type compatibility, while the Structure path withholds explicit semantic guidance during its first stage and identifies possible mentions through structural analysis. The Arbitration Agent then compares their rationales and confidence scores to adjudicate disagreements. Experiments on nine datasets with three backbone LLMs show that the two views are complementary: the Structure path substantially supplements entities missed by the Semantic path on some datasets, and arbitration outperforms existing multi-agent methods in most evaluated settings. Source code is available at https://github.com/MrHoux/SSDA-NER.

AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and expression origin may differ. We cast mixed-origin detection as dimension-to-composition source attribution, inferring content origin and expression origin before composing them into four collaboration types. We propose *Dimension-to-Composition Routing* (**D2C-Routing**), which routes content-side and expression-side evidence to supervised dimension heads before a learned gated composition layer predicts the final label. On MixD2C, a reconstructed split derived from the HART mixed-origin benchmark, our disclosed D2C-Routing-based detector system reaches 0.8603 four-way Avg TPR@1%FPR, 6.5 points above the same-split RACE-local rerun. Core ablations support the routing design, while error analysis shows that distinguishing AI-content/human-expression from fully AI-generated text remains the hardest boundary. Code is available at https://github.com/bystander563/d2c-routing-artifact.

The de facto way of utilizing black-box large language models (LLMs) to perform various downstream tasks is prompting. However, obtaining suitable prompts for specific tasks is still a challenging problem. While existing LLM-based methods demonstrate promising performance in task-oriented dialogue (TOD) task, they often require manual adjustment in prompt selection, or focus solely on dialogue understanding or generation. To address these issues, we propose an adaptive prompt generation framework to fully unleash the potential of LLMs for the comprehensive TOD system. Firstly, we design a trainable slot generator (TSG) that can generate domain and slot information in the belief state, which serves as prior knowledge for subsequent prompt generation. Next, we propose an adaptive prompt generator (APG) that utilizes the prior knowledge to generate prompts for the LLM, deriving the belief state and system response of the dialogue for evaluation. Finally, we evaluate our framework on the MultiWOZ 2.0 dataset. Extensive experiments demonstrate that our method outperforms existing methods. Our code and data will be released.

新闻文本通常会涉及多个地域,主地域则描述了文本舆情内容的地域属性,是进行舆情分析的关键属性。目前深度学习领域针对主地域自动抽取的研究还比较少。基于此,本文构建了一个基于IDLSTM+CRF的主地域抽取系统。该系统通过地名识别、主地域抽取、主地域补全三大模块实现对主地域标签的自动抽取和补全。在公开数据集上的实验结果表明,我们的方法在地名识别任务上要优于BiLSTM+CRF等模型。而对于主地域抽取任务,目前还没有标准的中文主地域评测集合。针对该问题,我们标注并开源了1226条验证集和1500条测试集。最终,我们的主地域抽取系统在两个集合上分别取得了91.7%和84.8%的抽取准确率,并成功运用于线上生产环境。

Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation.