A Survey of Knowledge-enhanced Text Generation
- W. Yu, Wenhao Yu, Meng Jiang
- Computer ScienceACM Computing Surveys
- 9 October 2020
A comprehensive review of the research on knowledge-enhanced text generation over the past five years is presented, which includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data.
Describing a Knowledge Base
- Qingyun Wang, Xiaoman Pan, Kevin Knight
- Computer ScienceInternational Conference on Natural Language…
- 6 September 2018
This work builds a generation framework based on a pointer network which can copy facts from the input KB, and adds two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to captured the inter-dependencies among related slots.
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
- Qingyun Wang, Manling Li, B. Onyshkevych
- Computer ScienceNorth American Chapter of the Association for…
- 1 July 2020
A novel and comprehensive knowledge discovery framework, COVID-KG, to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature and exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation.
Stage-wise Fine-tuning for Graph-to-Text Generation
- Qingyun Wang, Semih Yavuz, Victoria Lin, Heng Ji, Nazneen Rajani
- Computer ScienceAnnual Meeting of the Association for…
- 17 May 2021
This paper proposes a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph- to-text generation, and proposes a novel tree-level embedding method to capture the inter-dependency structures of the input graph.
Paper Abstract Writing through Editing Mechanism
- Qingyun Wang, Zhihao Zhou, Kevin Knight
- Computer ScienceAnnual Meeting of the Association for…
- 15 May 2018
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel…
PaperRobot: Incremental Draft Generation of Scientific Ideas
- Qingyun Wang, Lifu Huang, Yi Luan
- Computer ScienceAnnual Meeting of the Association for…
- 20 May 2019
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing…
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
- Qingyun Wang, Qi Zeng, Lifu Huang, Kevin Knight, Heng Ji, Nazneen Rajani
- Computer ScienceInternational Conference on Natural Language…
- 13 October 2020
A novel ReviewRobot is built to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison, and can serve as an assistant for paper reviewers, program chairs and authors.
GAIA - A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System
- Tongtao Zhang, Ananya Subburathinam, D. Wang
- EducationText Analysis Conference
- 2018
This research highlights the need to understand more fully the role of emotion in human interaction and the role that language plays in the development of language and culture.
A Two-Layer Dialogue Framework For Authoring Social Bots
- Jieming Ji, Qingyun Wang, Craig Carlson
- Computer Science
- 2017
This work explored creating a social bot for casual conversations, and found that in general supporting a richer set of conversational activities is desirable, and the users are more in favor of having natural conversations over menu-based conversations.
GAIA at SM-KBP 2020-A Dockerized Multi-media Multi-lingual Knowledge Extraction, Clustering, Temporal Tracking and Hypothesis Generation System
- Manling Li, Ying Lin, D. Wang
- Environmental Science
- 2021
Manling Li, Ying Lin, Tuan Manh Lai, Xiaoman Pan, Haoyang Wen, Sha Li Zhenhailong Wang, Pengfei Yu, Lifu Huang, Di Lu, Qingyun Wang Haoran Zhang, Qi Zeng, Chi Han, Zixuan Zhang, Yujia Qin Xiaodan Hu,…
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