Fine-grained Entity Typing (FET) has made great progress based on distant supervision but still suffers from label noise.Existing FET noise learning methods rely on prediction distributions in an instance-independent manner, which causes the problem of confirmation bias.In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.FCLC first train a coarse backbone model as a feature extractor and noise estimator.Loss correction is then applied to each feature cluster, learning directly from the noisy labels.Experimental results on three public datasets show that FCLC achieves the best performance over existing competitive systems.Auxiliary experiments further demonstrate that FCLC is stable to hyperparameters and it does help mitigate confirmation bias.We also find that in the extreme case of no clean data, the FCLC framework still achieves competitive performance.

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding, while in the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary. Empirical results show that the proposed architecture brings substantial improvements over baselines on the Multi-Xscience dataset.

Entity annotation in Wikipedia (officially named wikilinks) greatly benefits human end-users. Human editors are required to select all mentions that are most helpful to human end-users and link each mention to a Wikipedia page. We aim to design an automatic system to generate Wikipedia-style entity annotation for any plain text. However, existing research either rely heavily on mention-entity map or are restricted to named entities only. Besides, they neglect to select the appropriate mentions as Wikipedia requires. As a result, they leave out some necessary annotation and introduce excessive distracting annotation. Existing benchmarks also skirt around the coverage and selection issues. We propose a new task called Mention Detection and Se-lection for entity annotation, along with a new benchmark, WikiC, to better reflect annotation quality. The task is coined centering mentions specific to each position in high-quality human-annotated examples. We also proposed a new framework, DrWiki, to fulfill the task. We adopt a deep pre-trained span selection model inferring directly from plain text via tokens' context embedding. It can cover all possible spans and avoid limiting to mention-entity maps. In addition, information of both inarguable mention-entity pairs, and mention repeat has been introduced as token-wise representation enhancement by FLAT attention and repeat embedding respectively. Empirical results on WikiC show that, compared with often adopted and state-of-the-art Entity Linking and Entity Recognition methods, our method achieves improvement to previous methods in overall performance. Additional experiments show that DrWiki gains improvement even with a low-coverage mention-entity map.

Current state-of-the-art approaches for image captioning mainly apply the encoder-decoder framework with attention mechanisms, most of which ignore interactions between different types of image features and perform attention operations only once per word. The mentioned problems limit the captioning model’s capability to capture sufficient information to generate high-quality captions. By contrast, humans often rethink to polish up descriptions by re-focusing on more correct and important information, which is hard to capture at first glance. In this paper, we introduce a novel topic-guided captioning model to imitate such a human’s rethinking process by modeling interactions between visual and hierarchical semantic features of topics. To the best of our knowledge, we are the first to effectively consider hierarchical semantic features as guidance to facilitate visual attention, achieving human-like rethinking for captioning. Extensive experiments on the MS COCO dataset show that our proposed model achieves superior performance over state-of-the-art methods.