1 code implementation • 10 Dec 2022 • Yue Feng, Gerasimos Lampouras, Ignacio Iacobacci
To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses.
1 code implementation • ACL 2021 • Yue Feng, Yang Wang, Hang Li
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system.
Ranked #1 on Multi-domain Dialogue State Tracking on SGD
1 code implementation • ICCV 2023 • Yi-Ling Qiao, Alexander Gao, Yiran Xu, Yue Feng, Jia-Bin Huang, Ming C. Lin
Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline.
1 code implementation • Findings (NAACL) 2022 • Zhengxiang Shi, Yue Feng, Aldo Lipani
In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.
1 code implementation • Findings (ACL) 2022 • Fanghua Ye, Yue Feng, Emine Yilmaz
In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels.
1 code implementation • 26 May 2023 • Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai
Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes.
1 code implementation • 23 May 2023 • Yue Feng, Hossein A. Rahmani, Aldo Lipani, Emine Yilmaz
Task-oriented dialogue systems aim at providing users with task-specific services.
1 code implementation • 25 May 2023 • Hossein A. Rahmani, Xi Wang, Yue Feng, Qiang Zhang, Emine Yilmaz, Aldo Lipani
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation.
1 code implementation • 14 Jun 2023 • Zhiyuan Hu, Chumin Liu, Yue Feng, Anh Tuan Luu, Bryan Hooi
Controllable text generation is a challenging and meaningful field in natural language generation (NLG).
1 code implementation • 11 Jul 2023 • Fuxiao Liu, Paiheng Xu, Zongxia Li, Yue Feng
We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs).
no code implementations • 29 Apr 2019 • Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.
no code implementations • ACL 2020 • Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li
Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.
no code implementations • ACL 2022 • Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yilmaz
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
Dialogue State Tracking Multi-domain Dialogue State Tracking +1
no code implementations • Findings (NAACL) 2022 • Yue Feng, Zhen Han, Mingming Sun, Ping Li
DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.
no code implementations • 18 Mar 2023 • Zhen Han, Yue Feng, Mingming Sun
Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices.
1 code implementation • 16 Jun 2023 • Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani
This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.
no code implementations • 3 Jul 2023 • Ruiyun Xu, Yue Feng, Hailiang Chen
In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks.
no code implementations • 11 Aug 2023 • Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
no code implementations • 16 Sep 2023 • Zhiyuan Hu, Yue Feng, Yang Deng, Zekun Li, See-Kiong Ng, Anh Tuan Luu, Bryan Hooi
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.