1 code implementation • 15 Sep 2022 • Yunyi Yang, Hong Ding, Qingyi Liu, Xiaojun Quan
This paper studies the exposure bias problem in task-oriented dialog systems, where the model's generated content over multiple turns drives the dialog context away from the ground-truth distribution at training time, introducing error propagation and damaging the robustness of the TOD system.
no code implementations • 8 Mar 2022 • Hua Lu, Zhen Guo, Chanjuan Li, Yunyi Yang, Huang He, Siqi Bao
In recent years, Internet memes have been widely used in online chatting.
1 code implementation • EMNLP (NLP4ConvAI) 2021 • Xin Tian, Liankai Huang, Yingzhan Lin, Siqi Bao, Huang He, Yunyi Yang, Hua Wu, Fan Wang, Shuqi Sun
In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.
Ranked #1 on
Dialogue State Tracking
on Wizard-of-Oz
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
1 code implementation • Findings (ACL) 2021 • Yunhao Li, Yunyi Yang, Xiaojun Quan, Jianxing Yu
In this paper, we propose a retrieve-and-memorize framework to enhance the learning of system actions.
1 code implementation • ACL 2021 • Weizhou Shen, Siyue Wu, Yunyi Yang, Xiaojun Quan
In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea.
Ranked #10 on
Emotion Recognition in Conversation
on DailyDialog
1 code implementation • 7 Dec 2020 • Yunyi Yang, Yunhao Li, Xiaojun Quan
This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level.
1 code implementation • COLING 2020 • Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su
One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms.
Aspect Term Extraction and Sentiment Classification
Sentence
+1
1 code implementation • ACL 2020 • Kai Wang, Weizhou Shen, Yunyi Yang, Xiaojun Quan, Rui Wang
Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2