The Knowledge Graph logical reasoning task faces a dual challenge of insufficient semantic coverage from type information and missing structural information from relations. Although type annotations provide semantic priors for entities, their coarse-grained features cannot comprehensively characterize entity attributes; conversely, relational structure can enhance semantic representation, but the incompleteness of edges in real-world graphs limits modeling when relying on a single information source. To address these issues, we propose MRCLQR (Multi-information Relation Constraint-based Logical Query Reasoning), a framework with three core innovations: (1) an Information Semantic Alignment module based on contrastive learning, which achieves cross-modal semantic collaboration via entity-type-structure pairing; (2) a Constraint-aware Relation Encoding method that decomposes relation semantics into domain aggregation features, relation ontology semantics, and range constraint features; and (3) Neural-Symbolic Operators guided by domain constraints, which narrow the reasoning space through a constraint-aware attention mechanism. Experiments on FB15k, FB15k-237, and NELL-995 demonstrate that MRCLQR achieves average MRR scores of 35.8%, 16.2%, and 19.6%, respectively improving over the strongest baselines by 0.5%, 0.2%, and 0.2% --- and exhibits an 8.0% average gain on complex queries involving negation. Ablation studies validate the effectiveness of multi-source collaboration and the curriculum learning strategy. This work offers a novel paradigm for heterogeneous knowledge fusion and logical query reasoning.

Trajectory metric learning, which supports the trajectory similarity search, is one of the most fundamental tasks in spatial-temporal data analysis. However, existing trajectory metric learning methods rely on massive labels of pairwise trajectory distance, and thus cannot be applied to few-shot scenarios frequently occurring in real-world applications. Though performance drops caused by insufficient labels can be alleviated by knowledge distillation, we demonstrate that they cannot be directly applied to few-shot trajectory metric learning due to the domain shift problem. To this end, this paper proposes invariant and relaxed learning enhanced knowledge distillation method TMLKD for few-shot trajectory metric learning, such that domain-invariant representation and rank knowledge can be distilled. Specifically, in the representation learning phase, it first employs an adversarial sub-network to distinguish domain-specific and domain-invariant information, so as to distill transferable representation knowledge from teacher models. To mitigate the few-shot problem in student model training, we further enrich sparse labels of the target domain by utilizing the rank knowledge revealed in teachers' predictions. Particularly, TMLKD employs a list-wise learning-to-rank approach to learn the relaxed trajectory ranking orders instead of focusing on all the samples inefficiently. Finally, to guide accurate distillation, we adaptively assign reliability of teacher prediction by utilizing the ground-truth labels, to avoid misleading the student model with low-quality teacher predictions. Extensive experiments on three real-world datasets demonstrate the superiority of our model.

The automatic generation of radiological imaging reports aims to produce accurate and coherent clinical descriptions based on X-ray images. This facilitates clinicians in completing the arduous task of report writing and advances clinical automation. The primary challenge in radiological imaging report generation lies in accurately capturing and describing abnormal regions in the images under data bias conditions, resulting in the generation of lengthy texts containing image details. Existing methods mostly rely on prior knowledge such as medical knowledge graphs, corpora, and image databases to assist models in generating more precise textual descriptions. However, these methods still struggle to identify rare anomalies in the images. To address this issue, we propose a two-stage training model, named CLR2G, based on cross-modal contrastive learning. This model delegates the task of capturing anomalies, particularly those challenging for the generative model trained with cross-entropy loss under data bias conditions, to a specialized abnormality capture component. Specifically, we employ a semantic matching loss function to train additional abnormal image and text encoders through cross-modal contrastive learning, facilitating the capture of 13 common anomalies. We utilize the anomalous image features, text features and their confidence probabilities as a posteriori knowledge to help the model generate accurate image reports. Experimental results demonstrate the state-of-the-art performance of our method on two widely used public datasets, IU-Xray and MIMIC-CXR.

Sequential Recommendation (SR) has received increasing attention due to its ability to capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an effective approach for sequential recommendation by learning invariance from different views of an input. However, most existing data or model augmentation methods may destroy semantic sequential interaction characteristics and often rely on the hand-crafted property of their contrastive view-generation strategies. In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first introduces a simple yet effective augmentation method called Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational AutoEncoders (VAE) as the view generator and can constitute contrastive views while preserving the original sequence's semantics. Next, the model employs a meta-optimized two-step training strategy, which aims to adaptively generate contrastive views without relying on manually designed view-generation techniques. Finally, we evaluate our proposed method Meta-SGCL using three public real-world datasets. Compared with the state-of-the-art methods, our experimental results demonstrate the effectiveness of our model and the code is available.11https.//anonymous.4open.science/status/Meta-SGCL-05B5

Graph Collaborative Filtering (GCF) is designed to leverage high-order connectivity in user-item graphs, thereby significantly enhancing recommendation performance. Recent advancements have seen the integration of contrastive learning into GCF as a strategy to mitigate the challenges of data sparsity. This approach involves creating contrastive views through augmentations, followed by the generation of self-supervised signals. These signals are produced by maximizing the mutual information between the contrastive views. While this method has proven effective, we argue that current CL-based GCF models are still limited to current augmentation techniques. Existing data augmentation or noise perturbation may destroy the structural and semantic features of the original data and node attribute information is not considered. To tackle the above limitations, we propose a Meta-optimized Structure and Semantic Contrastive Learning for Graph Collaborative Filtering, named Meta-SSCL, which utilizes graph structure information and semantic information contrastive learning for recommendation. Specifically, we first model the structural and node semantic information representations with LightGCN and vanilla attention mechanism, respectively. Then consider the structural and semantic information as two contrastive views for recommendation. Next, the meta-optimized two-step training strategy generates adaptive contrastive views. Finally, we fuse structural and semantic representations for recommendation. Extensive experiments on real-world datasets demonstrate that Meta-SSCL consistently outperforms state-of-the-art sequential recommendation methods. The code is available1.

Multi-modal data have recently been utilized to improve the performance of knowledge graph completion (KGC), attracting widespread research interest. However, they have been ignored in few-shot knowledge graph completion (FKGC), which aims to discover potential facts involving unseen relations that only appear in few-shot triples. The most relevant FKGC study simply concatenates various modal features, but the performance is still limited due to the following problems: (1) lack of exploiting significant multi-modal features in neighborhoods, and (2) ineffectively modeling inter-modal interactions in a few-shot setting. To tackle these problems, we propose a novel relational learning model entitled MMSN (Multi-Modal Siamese Network) for few-shot knowledge graph completion, which is composed of the following two primary modules: the Siamese multi-modal neighbor encoder (SMNE) and the meta-learning multi-modal knowledge representation decoder (MKRD). The module SMNE is developed to encode diverse modalities of neighbors by a Siamese attention network, fuse multi-modal information through a gating fusion network, and learn effective relational embeddings using an aggregator. The module MKRD is introduced to handle inter-modal interactions between multiple modalities and train the proposed model in a few-shot scenario. Extensive experiments demonstrate that our proposed model MMSN outperforms the state-of-the-art FKGC models, including uni-modal and multi-modal models, on two real-world few-shot multi-modal datasets.

Extracting entities and relations from text is a significant task of information extraction. Existing extraction models often straightforwardly produce their confident prediction results without any reconsideration or double-checking, resulting in avoidable mistakes and sub-optimal performance. In this paper, we propose a novel coarse-to-fine extraction framework, which first extracts high-potential relations as well as entities via knowledge distillation, and then rechecks the predictions via handcrafted natural language inference (NLI) task in a fine-grained manner. Specifically, based on the knowledge distillation mechanism, we train multiple teacher models iteratively through an adaptive loss function for making one teacher concentrate more on the data that others are incompetent for. Then, these complementary teacher models are utilized to provide valuable soft-label information for training a considerate student model, enabling it to generate reliable preliminary predictions. Further, these generated potential relations and entities are formulated as hypotheses, together with the original sentences as premises, serving as the input for an NLI model. Considering the linguistic diversity of relational expression, we automatically generate various semantic templates for hypotheses through an$\mathcal{N}$-gram mining strategy. Moreover, due to the existence of multi-fact sentences, a relation-guided Gaussian attention is designed to reduce the gap between the single-relation hypothesis and the multi-relation premise. To implement efficient training, we also develop several ways to generate high-quality negative samples, which help the NLI model learn to identify errors. Experimental results show that the proposed method is effective and outperforms other strong baselines on public benchmarks.

Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. Compared with the sentence-level counterpart, it has raised two significant challenges: a) In most cases, a relational fact can be adequately expressed via a small subset of sentences from the document, namely evidence. But the traditional method cannot model such strong semantic correlations between evidence sentences that collaborate to describe a specific relation; b) The data of this task is extremely long-tail in terms of too many NA instances and imbalanced relational types. Such data can mislead the tail prediction bias to the head categories in the RE model. In this paper, we present a novelEvidence reasoning andCurriculum learning method forDocRE(DRE-EC) to address these challenges. Particularly, we first formulate evidence extraction as a sequential decision problem through a crafted reinforcement learning mechanism with an efficient path searching strategy to reduce the action space. Providing the evidence for each entity pair as a customized-filtered document in advance helps infer the relations better. To address the long-tail issue, we further develop a hybrid curriculum learning method at the NA-level (NC) and relation-level (RC) with our customized difficulty measure score. In NC, the NA samples are scheduled in an easy-to-hard scheme and gradually added, resulting in the data distribution from ideal and balanced to real and unbalanced. In RC, the scheme is switched into hard-to-easy to enhance the hard and tail samples. In addition, we propose a new Equalization adaptive Focal Loss(EFLoss) that can adjust to the changing data distribution and focus more on the tail categories. We conduct various experiments on two document-level RE benchmarks and achieve a remarkable improvement over previous competitive baselines. Furthermore, we provide detailed analyses of the advantages and effectiveness of our method.

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features; (2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.

The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.

Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language. CLS has attracted wide research attention due to its practical significance in the multi-lingual world. Though great contributions have been made, existing CLS works typically focus on short documents, such as news and guides. Different from these short texts, long documents such as academic articles usually discuss complicated subjects and consist of thousands of words, making them non-trivial to process and summarize. To promote CLS research on long documents, we construct Perseus, the first long-document CLS dataset which collects about 94K Chinese scientific documents paired with English summaries. The average length of documents in Perseus is more than 2000 tokens. As a preliminary study on long-document CLS, we build and evaluate various CLS baselines, including pipeline and end-to-end methods. Experimental results on Perseus show the superiority of the end-to-end baseline, which performs the best among all methods. Furthermore, to provide a deeper understanding, we manually analyze the model outputs and discuss specific challenges faced by current approaches. We hope that our work could benchmark long-document CLS and benefit future studies.

Distant supervision is an efficient way to generate large-scale training data for relation extraction without human efforts. However, the accompanying challenges have been plaguing the advance of the extractor: (1) the automatically annotated labels for training data contain much noisy data; (2) the annotations, based on bag-level (cluster of sentences) instead of sentence-level (single sentence), are too coarse to train an accurate extractor; (3) hetergeneous sentences are hard for a denoising model to capture the underlying commonness among valid relational expressions. To address these issues, we bulid a novel sentence representation and craft reinforcement learning to select the expressive sentence for each relation mentioned in a bag. More specifically, we introduce entity-free sentence pattern incorporated with attentive type information. Furthermore, multiple interactions between entity-specific and entity-free representation are proposed to generate complementary sentence features (for challenge 3). Then we design a fine-grained reward function, and model the sentence selection process as an auction where different relations for a bag need to compete together to achieve the possession of a specific sentence based on its expressiveness(for challenge 1 and 2). The experimental results on two public datasets demonstrate the superiority of our model for distantly supervised relation extraction.

Neural Relation Extraction (RE) models need large amounts of labeled data for effective training, which mainly comes from automatically labeling by Distant Supervision (DS). Though fast and easy, the label shift problem inevitably happens, i.e., the label distribution of DS-generated training set is quite different from that of the real world (i.e. test set). According to our observations, label shift not only leads to performance diminishment, but also hinders the reliability of DS-RE models by causing bad confidence estimation. In this paper, we make contributions by answering the following three questions: 1) How to improve performance of DS-RE models under label shift? 2) How to make sure their reliability under label shift? 3) How to improve both performance and reliability for DS-RE models under label shift? To the best of our knowledge, this is the first paper to study the performance as well as reliability of DS-RE models under label shift. Experiment results show significant improvements on two real-world datasets and six popular neural RE models, making a step further towards high-performance and reliable RE system under real-world label-shift conditions.

Entity alignment is a crucial task in knowledge graph fusion. However, most entity alignment approaches have the scalability problem. Recent methods address this issue by dividing large KGs into small blocks for embedding and alignment learning in each. However, such a partitioning and learning process results in an excessive loss of structure and alignment. Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives. First, we propose a centrality-based subgraph generation algorithm to recall some landmark entities serving as the bridges between different subgraphs. Second, we introduce self-supervised entity reconstruction to recover entity representations from incomplete neighborhood subgraphs, and design cross-subgraph negative sampling to incorporate entities from other subgraphs in alignment learning. Third, during the inference process, we merge the embeddings of subgraphs to make a single space for alignment search. Experimental results on the benchmark OpenEA dataset and the proposed large DBpedia1M dataset verify the effectiveness of our approach.

Document-level Relation Extraction (RE) is a promising task aiming at identifying relations of multiple entity pairs in a document. However, in most cases, a relational fact can be expressed enough via a small subset of sentences from the document, namely evidence sentence. Moreover, there often exist strong semantic correlations between evidence sentences that collaborate together to describe a specific relation. To address these challenges, we propose a novel evidence-aware model for document-level RE. Particularly, we formulate evidence sentence selection as a sequential decision problem through a crafted reinforcement learning mechanism. Considering the explosive search space of our agent, an efficient path searching strategy is executed on the converted document graph to heuristically obtain hopeful sentences and feed them to reinforcement learning. Finally, each entity pair owns a customized-filtered document for further inferring the relation between them. We conduct various experiments on two document-level RE benchmarks and achieve a remarkable improvement over previous competitive baselines, verifying the effectiveness of our method.

Sports game summarization aims at generating sports news from live commentaries. However, existing datasets are all constructed through automated collection and cleaning processes, resulting in a lot of noise. Besides, current works neglect the knowledge gap between live commentaries and sports news, which limits the performance of sports game summarization. In this paper, we introduce K-SportsSum, a new dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players. Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news. Extensive experiments on K-SportsSum and SportsSum datasets show that our model achieves new state-of-the-art performances. Qualitative analysis and human study further verify that our model generates more informative sports news.

Sports game summarization aims to generate news articles from live text commentaries. A recent state-of-the-art work, SportsSum, not only constructs a large benchmark dataset, but also proposes a two-step framework. Despite its great contributions, the work has three main drawbacks: 1) the noise existed in SportsSum dataset degrades the summarization performance; 2) the neglect of lexical overlap between news and commentaries results in low-quality pseudo-labeling algorithm; 3) the usage of directly concatenating rewritten sentences to form news limits its practicability. In this paper, we publish a new benchmark dataset SportsSum2.0, together with a modified summarization framework. In particular, to obtain a clean dataset, we employ crowd workers to manually clean the original dataset. Moreover, the degree of lexical overlap is incorporated into the generation of pseudo labels. Further, we introduce a reranker-enhanced summarizer to take into account the fluency and expressiveness of the summarized news. Extensive experiments show that our model outperforms the state-of-the-art baseline.

The development of existing extractive summarization models for long-form document summarization is hindered by two factors: 1) the computation of the summarization model will dramatically increase due to the sheer size of the input long document; 2) the discourse structural information in the long-form document has not been fully exploited. To address the two deficiencies, we propose HEROES, a novel extractive summarization model for summarizing long-form documents with rich discourse structural information. In particular, the HEROES model consists of two modules: 1) a content ranking module that ranks and selects salient sections and sentences to compose a short digest that empowers complex summarization models and serves as its input; 2) an extractive summarization module based on a heterogeneous graph with nodes from different discourse levels and elaborately designed edge connections to reflect the discourse hierarchy of the document and restrain the semantic drifts across section boundaries. Experimental results on benchmark datasets show that HEROES can achieve significantly better performance compared with various strong baselines.

Distant supervision is an efficient way to generate large-scale training data for relation extraction without human efforts. However, a coin has two sides. The automatically annotated labels for training data are problematic, which can be summarized as multi-instance multi-label problem and coarse-grained (bag-level) supervised signal. To address these problems, we propose two reasonable assumptions and craft reinforcement learning to capture the expressive sentence for each relation mentioned in a bag. More specifically, we extend the original expressed-at-least-once assumption to multi-label level, and introduce a novel express-at-most-one assumption. Besides, we design a fine-grained reward function, and model the sentence selection process as an auction where different relations for a bag need to compete together to achieve the possession of a specific sentence based on its expressiveness. In this way, our model can be dynamically self-adapted, and eventually implements the accurate one-to-one mapping from a relation label to its chosen expressive sentence, which serves as training instances for the extractor. The experimental results on a public dataset demonstrate that our model constantly and substantially outperforms current state-of-the-art methods for relation extraction.

The recent art in relation extraction is distant supervision which generates training data by heuristically aligning a knowledge base with free texts and thus avoids human labelling. However, the concerned relation mentions often use the bag-of-words representation, which ignores inner correlations between features located in different dimensions and makes relation extraction less effective. To capture the complex characteristics of relation expression and tighten the correlated features, we attempt to discover and utilise informative correlations between features by the following four phases: 1) formulating semantic similarities between lexical features using the embedding method; 2) constructing generative relation for lexical features with different sizes of side windows; 3) computing correlation scores between syntactic features through a kernel-based method; and 4) conducting a distillation process for the obtained correlated feature pairs and integrating informative pairs with existing relation extraction models. The extensive experiments demonstrate that our method can effectively discover correlation information and improve the performance of state-of-the-art relation extraction methods.