Peng Wu


GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking
Peng Wu | Bowei Zou | Ridong Jiang | AiTi Aw
Findings of the Association for Computational Linguistics: EMNLP 2020

As an essential component of task-oriented dialogue systems, Dialogue State Tracking (DST) takes charge of estimating user intentions and requests in dialogue contexts and extracting substantial goals (states) from user utterances to help the downstream modules to determine the next actions of dialogue systems. For practical usages, a major challenge to constructing a robust DST model is to process a conversation with multi-domain states. However, most existing approaches trained DST on a single domain independently, ignoring the information across domains. To tackle the multi-domain DST task, we first construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains. Then, we encode the graph information of dialogue states by graph convolutional networks and utilize a hard copy mechanism to directly copy historical states from the previous conversation. Experimental results show that our model improves the performances of the multi-domain DST baseline (TRADE) with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets, respectively.


Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering
Peng Wu | Shujian Huang | Rongxiang Weng | Zaixiang Zheng | Jianbing Zhang | Xiaohui Yan | Jiajun Chen
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Relation detection is a core step in many natural language process applications including knowledge base question answering. Previous efforts show that single-fact questions could be answered with high accuracy. However, one critical problem is that current approaches only get high accuracy for questions whose relations have been seen in the training data. But for unseen relations, the performance will drop rapidly. The main reason for this problem is that the representations for unseen relations are missing. In this paper, we propose a simple mapping method, named representation adapter, to learn the representation mapping for both seen and unseen relations based on previously learned relation embedding. We employ the adversarial objective and the reconstruction objective to improve the mapping performance. We re-organize the popular SimpleQuestion dataset to reveal and evaluate the problem of detecting unseen relations. Experiments show that our method can greatly improve the performance of unseen relations while the performance for those seen part is kept comparable to the state-of-the-art.


Abstractive Summarization of Line Graphs from Popular Media
Charles Greenbacker | Peng Wu | Sandra Carberry | Kathleen McCoy | Stephanie Elzer
Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages

Improving the Accessibility of Line Graphs in Multimodal Documents
Charles F. Greenbacker | Peng Wu | Sandra Carberry | Kathleen F. McCoy | Stephanie Elzer | David D. McDonald | Daniel Chester | Seniz Demir
Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies