Search Results for author: Kexin Wang

Found 20 papers, 13 papers with code

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

Do we listen to what we are told? An empirical study on human behaviour during the COVID-19 pandemic: neural networks vs. regression analysis

no code implementations21 Nov 2023 Yuxi Heluo, Kexin Wang, Charles W. Robson

In this work, we contribute the first visual open-source empirical study on human behaviour during the COVID-19 pandemic, in order to investigate how compliant a general population is to mask-wearing-related public-health policy.

object-detection Object Detection +1

SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval

1 code implementation19 Jul 2023 Nandan Thakur, Kexin Wang, Iryna Gurevych, Jimmy Lin

In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval.

Information Retrieval Retrieval

MOSPC: MOS Prediction Based on Pairwise Comparison

no code implementations18 Jun 2023 Kexin Wang, Yunlong Zhao, Qianqian Dong, Tom Ko, Mingxuan Wang

And our framework also surpasses the strong baseline in ranking accuracy on each fine-grained segment.

DAPR: A Benchmark on Document-Aware Passage Retrieval

3 code implementations23 May 2023 Kexin Wang, Nils Reimers, Iryna Gurevych

This drives us to build a benchmark for this task including multiple datasets from heterogeneous domains.

Passage Retrieval Retrieval

UKP-SQuARE v3: A Platform for Multi-Agent QA Research

1 code implementation31 Mar 2023 Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych

To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents.

Question Answering

UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA

1 code implementation19 Aug 2022 Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych

In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations.

Adversarial Attack Explainable Models +2

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

UKP-SQUARE: An Online Platform for Question Answering Research

1 code implementation ACL 2022 Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Max Eichler, Gregor Geigle, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Gül Şahin, Iryna Gurevych

Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).

Explainable Models Information Retrieval +2

An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary

no code implementations4 Mar 2021 Shuangyong Song, Kexin Wang, Chao Wang, Haiqing Chen, Huan Chen

In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses.

Response Generation

Understanding Memory Modules on Learning Simple Algorithms

no code implementations1 Jul 2019 Kexin Wang, Yu Zhou, Shaonan Wang, Jiajun Zhang, Cheng-qing Zong

Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms.

Dimensionality Reduction

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