Search Results for author: Jianfeng Qu

Found 18 papers, 12 papers with code

Knowledge Enhanced Sports Game Summarization

1 code implementation24 Nov 2021 Jiaan Wang, Zhixu Li, Tingyi Zhang, Duo Zheng, Jianfeng Qu, An Liu, Lei Zhao, Zhigang Chen

Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news.

Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks

1 code implementation29 Jan 2022 Jiaan Wang, Beiqi Zou, Zhixu Li, Jianfeng Qu, Pengpeng Zhao, An Liu, Lei Zhao

Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context.

Multi-Task Learning

ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization

2 code implementations11 Feb 2022 Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, Jie zhou

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents.

Ensemble Semi-supervised Entity Alignment via Cycle-teaching

1 code implementation12 Mar 2022 Kexuan Xin, Zequn Sun, Wen Hua, Bing Liu, Wei Hu, Jianfeng Qu, Xiaofang Zhou

We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher.

Entity Alignment Knowledge Graphs

A Survey on Cross-Lingual Summarization

no code implementations23 Mar 2022 Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou

Cross-lingual summarization is the task of generating a summary in one language (e. g., English) for the given document(s) in a different language (e. g., Chinese).

RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation

1 code implementation17 Jul 2022 Kexin Wang, Zhixu Li, Jiaan Wang, Jianfeng Qu, Ying He, An Liu, Lei Zhao

Nevertheless, the correlations between knowledge implied in the multi-turn context and the transition regularities between relations in KGs are under-explored.

Dialogue Generation Knowledge Graphs +2

Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding

1 code implementation23 Aug 2022 Kexuan Xin, Zequn Sun, Wen Hua, Wei Hu, Jianfeng Qu, Xiaofang Zhou

Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives.

Entity Alignment

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

no code implementations3 Sep 2022 Shangfei Zheng, Weiqing Wang, Jianfeng Qu, Hongzhi Yin, Wei Chen, Lei Zhao

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.

Knowledge Graphs Missing Elements +1

Long-Document Cross-Lingual Summarization

1 code implementation1 Dec 2022 Shaohui Zheng, Zhixu Li, Jiaan Wang, Jianfeng Qu, An Liu, Lei Zhao, Zhigang Chen

Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the given documents in another language.

Machine Translation

Zero-Shot Cross-Lingual Summarization via Large Language Models

no code implementations28 Feb 2023 Jiaan Wang, Yunlong Liang, Fandong Meng, Beiqi Zou, Zhixu Li, Jianfeng Qu, Jie zhou

Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language.

Informativeness

Is ChatGPT a Good NLG Evaluator? A Preliminary Study

1 code implementation7 Mar 2023 Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, Jie zhou

In detail, we regard ChatGPT as a human evaluator and give task-specific (e. g., summarization) and aspect-specific (e. g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models.

nlg evaluation Story Generation

Frequency Enhanced Hybrid Attention Network for Sequential Recommendation

1 code implementation18 Apr 2023 Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Victor S. Sheng

However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.

Contrastive Learning Sequential Recommendation

Towards Unifying Multi-Lingual and Cross-Lingual Summarization

no code implementations16 May 2023 Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie zhou

In this paper, we aim to unify MLS and CLS into a more general setting, i. e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language.

Language Modelling Text Summarization

Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model

no code implementations17 Jun 2023 Jiaan Wang, Jianfeng Qu, Yunlong Liang, Zhixu Li, An Liu, Guanfeng Liu, Xin Zheng

Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.

Knowledge Graphs

Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation

no code implementations21 Oct 2023 Yongjing Hao, Pengpeng Zhao, Junhua Fang, Jianfeng Qu, Guanfeng Liu, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou

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.

Contrastive Learning Sequential Recommendation

Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning

1 code implementation9 Jan 2024 Jiaan Wang, Jianfeng Qu, Kexin Wang, Zhixu Li, Wen Hua, Ximing Li, An Liu

Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (\emph{e. g.}, knowledge graphs; KGs).

Contrastive Learning Knowledge Graphs

Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly

1 code implementation CoNLL (EMNLP) 2021 Shisong Chen, Binbin Gu, Jianfeng Qu, Zhixu Li, An Liu, Lei Zhao, Zhigang Chen

Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity.

coreference-resolution

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