xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark

1 code implementation • 13 Oct 2023 • Chen Zhang, Luis Fernando D'Haro, Chengguang Tang, Ke Shi, Guohua Tang, Haizhou Li

The English dialogue data are extended to nine other languages with commercial machine translation systems.

Dialogue Evaluation Machine Translation

Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4

1 code implementation • 22 Jun 2023 • Mario Rodríguez-Cantelar, Chen Zhang, Chengguang Tang, Ke Shi, Sarik Ghazarian, João Sedoc, Luis Fernando D'Haro, Alexander Rudnicky

The advent and fast development of neural networks have revolutionized the research on dialogue systems and subsequently have triggered various challenges regarding their automatic evaluation.

Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration

no code implementations • 14 Jul 2022 • Zhenyu Zhang, Bowen Yu, Haiyang Yu, Tingwen Liu, Cheng Fu, Jingyang Li, Chengguang Tang, Jian Sun, Yongbin Li

In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.

Language Modelling

DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding

1 code implementation • 2 Dec 2021 • Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He, Jun Huang

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.

Knowledge Graphs Knowledge Probing +3

Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings

no code implementations • 29 Oct 2021 • Guanglin Niu, Yang Li, Chengguang Tang, Zhongkai Hu, Shibin Yang, Peng Li, Chengyu Wang, Hao Wang, Jian Sun

The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.

Knowledge Base Question Answering Knowledge Graph Embedding +1

Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion

1 code implementation • 27 Apr 2021 • Guanglin Niu, Yang Li, Chengguang Tang, Ruiying Geng, Jian Dai, Qiao Liu, Hao Wang, Jian Sun, Fei Huang, Luo Si

Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.

Few-Shot Learning Relational Reasoning

A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges

no code implementations • 26 Jul 2020 • Bin Fu, Yunqi Qiu, Chengguang Tang, Yang Li, Haiyang Yu, Jian Sun

Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases.

Information Retrieval Question Answering +2

Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

no code implementations • ACL 2020 • Yinpei Dai, Hangyu Li, Chengguang Tang, Yongbin Li, Jian Sun, Xiaodan Zhu

Existing end-to-end dialog systems perform less effectively when data is scarce.

Dialog Learning Goal-Oriented Dialog +1

A Survey on Dialog Management: Recent Advances and Challenges

no code implementations • 5 May 2020 • Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun

Dialog management (DM) is a crucial component in a task-oriented dialog system.

Management Reinforcement Learning (RL)