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20162019
most citedMulti-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

65 citations · 114 across the 4 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL2019

Entity, Relation, and Event Extraction with Contextualized Span Representations

David Wadden, Ulme Wennberg, Yi Luan +1

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our fram…

cs.CL2019

PaperRobot: Incremental Draft Generation of Scientific Ideas

Qingyun Wang, Lifu Huang, Zhiying Jiang +4

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 co…

cs.CL201938 cited

A General Framework for Information Extraction using Dynamic Span Graphs

Yi Luan, Dave Wadden, Luheng He +3

We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are constructed by s…

cs.CL2018

Monolingual sentence matching for text simplification

Yonghui Huang, Yunhui Li, Yi Luan

This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network struc…

cs.CL2018

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Yi Luan, Luheng He, Mari Ostendorf +1

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SciERC, a dataset that includes annot…

cs.CL201765 cited

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Yi Luan, Chris Brockett, Bill Dolan +2

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the pro…