13 accepted papers
Large language models (LLMs) have made significant strides in mathematical reasoning, particularly at the elementary level. However, they continue to face substantial challenges when confronted with complex, advanced mathematical problems. In contrast to humans—who can effectively draw upon prior ex
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud hanging over the LLM skyscraper. Structural knowledge prompting (SKP) is a prominent paradigm to integrate external knowl…
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can en- hance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural informatio…
Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given multi-modal knowledge graphs (MMKG), collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC…
Recently, deep learning-based methods has gained increasing attention in typhoon tasks. Due to different optimization targets, existing works apply multi-object detection to typhoon detection and keypoint detection to typhoon eye location. However, such two-stage methods ignored the internal connect…
Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summarize methods, which use extractors to simulate binar…
Query-focused meeting summarization (QFMS) aims to generate suimnaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism. However, du…
Zero-shot relation extraction aims to extract novel relations that are not observed beforehand. However, existing representation methods are not pre-trained for relational representations and embeddings contain much linguistic information, the distances between them are not consistent with relationa…
Forecasting rapid intensification (RI) of tropical cyclones (TC) is an important and challenging task. However, existing RI forecast methods pay little attention to the imbalanced distribution of RI with dynamic statistical models or ma-chine learning methods. Actually, RI prediction is a class-imba…
Supervised open relation extraction aims to discover novel relations by leveraging supervised data of pre-defined relations. However, most existing methods do not achieve effective knowledge transfer from pre-defined relations to novel relations, they have difficulties generating high-quality pseudo…
Open relation extraction is the task to extract relational facts without pre-defined relation types from open-domain corpora. However, since there are some hard or semi-hard instances sharing similar context and entity information but belonging to different underlying relation, current OpenRE method…
Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage. However, it still faces some challenges since the unseen relations of instances are similar or the input sentences have similar entities, the unseen relation representations from different…
Curvilinear structure segmentation (CSS) is under semantic segmentation, whose applications include crack detection, aerial road extraction, and biomedical image segmentation. In general, geometric topology and pixel-wise features are two critical aspects of CSS. However, most semantic segmentation…