Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding

Zhang, Tingyi; Li, Zhixu; Wang, Jiaan; Qu, Jianfeng; Lin, Yuan; Liu, An; Zhao, Lei; Chen, Zhigang

doi:10.1007/978-3-031-00129-1_10

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…We further analyze the model outputs and show the challenges still remain in sports game summarization. In the future, we would like to (1) explore the semi-supervised and multi-lingual settings on GOAL; (2) leverage graph structure to model the commentary information, and generate sports news in a graph-to-text manner (Feng et al, 2021b), or even consider the temporal information (i.e., t i ) in the graph structure (Zhang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

GOAL: Towards Benchmarking Few-Shot Sports Game Summarization

Wang¹,

Zhang²,

Shi³

2022

Preprint

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Sports game summarization aims to generate sports news based on real-time commentaries. The task has attracted wide research attention but is still under-explored probably due to the lack of corresponding English datasets. Therefore, in this paper, we release GOAL, the first English sports game summarization dataset. Specifically, there are 103 commentary-news pairs in GOAL, where the average lengths of commentaries and news are 2724.9 and 476.3 words, respectively. Moreover, to support the research in the semi-supervised setting, GOAL additionally provides 2,160 unlabeled commentary documents. Based on our GOAL, we build and evaluate several baselines, including extractive and abstractive baselines. The experimental results show the challenges of this task still remain. We hope our work could promote the research of sports game summarization. The dataset has been released at https: //github.com/krystalan/goal.

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Section: Discussionmentioning

confidence: 99%

GOAL: Towards Benchmarking Few-Shot Sports Game Summarization

Wang¹,

Zhang²,

Shi³

2022

Preprint

Self Cite

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“…Some methods (Zeng et al, 2022) improve their capability of dealing with large-scale KG pairs. Some methods (Jin et al, 2020;Li et al, 2021;Zhang et al, 2022) consider the multigranularity information via the addition between internal regularity and external influence.…”

Section: Knowledge Graph Representationmentioning

confidence: 99%

Active Temporal Knowledge Graph Alignment

Zhou

Zeng

et al. 2023

International Journal on Semantic Web and Information Systems

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Entity alignment aims to identify equivalent entity pairs from different knowledge graphs (KGs). Recently, aligning temporal knowledge graphs (TKGs) that contain time information has aroused increasingly more interest, as the time dimension is widely used in real-life applications. The matching between TKGs requires seed entity pairs, which are lacking in practice. Hence, it is of great significance to study TKG alignment under scarce supervision. In this work, the authors formally formulate the problem of TKG alignment with limited labeled data and propose to solve it under the active learning framework. As the core of active learning is to devise query strategies to select the most informative instances to label, the authors propose to make full use of time information and put forward novel time-aware strategies to meet the requirement of weakly supervised temporal entity alignment. Extensive experimental results on multiple real-world datasets show that it is important to study TKG alignment with scarce supervision, and the proposed time-aware strategy is effective.

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“…Knowledge-Grounded Dialogue (KGD) aims to generate an informative response based on a given dialogue context and external knowledge to improve the usefulness and meaningfulness of the generated responses (Ghazvininejad et al 2017;Zhou et al 2018;Liu et al 2019;Kim, Ahn, and Kim 2020;Li et al 2021;Rashkin et al 2021;Wu et al 2022;Sun et al 2023). As for the choice of knowledge source, structural knowledge graphs (KGs) are proven options (Moon et al 2019;Jung, Son, and Lyu 2020;Wu et al 2022), which consist of a lot of knowledge facts that are frequently used in daily life (Zheng et al 2021;Zhang et al 2022;Cao et al Have you ever heard about Leonardo? Oh, absolutely!…”

Section: Introductionmentioning

confidence: 99%

Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning

Wang,

Qu,

Wang

et al. 2024

AAAI

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Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (e.g., knowledge graphs; KGs). Recently, the emergence of large language models (LLMs) and pre-training techniques has brought great success to knowledge-grounded dialogue. However, when building KGD systems in real applications, there are various real-world noises that are inevitable to face. For example, the dialogue context might involve perturbations such as misspellings and abbreviations. In addition, KGs typically suffer from incompletion and also might contain erroneous and outdated facts. Such real-world noises pose a challenge to the robustness of KGD systems and hinder their applications in the real world. In this paper, we propose an entity-based contrastive learning framework for improving the robustness of KGD. Specifically, we make use of the entity information in a KGD sample to create both its positive and negative samples which involve semantic-irrelevant and semantic-relevant perturbations, respectively. The contrastive learning framework ensures the KGD model is aware of these two types of perturbations, thus could generate informative responses with the potentially noisy inputs in real applications. Experimental results on three widely-used benchmark datasets show that our method achieves new state-of-the-art performance in terms of automatic evaluation scores, verifying its effectiveness and potentiality. Furthermore, we show that our method is able to generate better responses than comparison models in both the noisy and the few-shot settings.

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Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding

Cited by 5 publications

References 31 publications

GOAL: Towards Benchmarking Few-Shot Sports Game Summarization

GOAL: Towards Benchmarking Few-Shot Sports Game Summarization

Active Temporal Knowledge Graph Alignment

Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning

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