activity
20172022
most citedArgument Mining for Understanding Peer Reviews

6 citations · 18 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CL2022

Efficient Argument Structure Extraction with Transfer Learning and Active Learning

Xinyu Hua, Lu Wang

The automation of extracting argument structures faces a pair of challenges on (1) encoding long-term contexts to facilitate comprehensive understanding, and (2) improving data eff…

cs.CL2021

DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation

Xinyu Hua, Ashwin Sreevatsa, Lu Wang

We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiri…

cs.CL20202 cited

PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation

Xinyu Hua, Lu Wang

Pre-trained Transformers have enabled impressive breakthroughs in generating long and fluent text, yet their outputs are often "rambling" without coherently arranged content. In th…

cs.CL2020

XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference

Xinyu Hua, Lei Li, Lifeng Hua +1

Automatic identification of mentioned entities in social media posts facilitates quick digestion of trending topics and popular opinions. Nonetheless, this remains a challenging ta…

cs.CL2019

Sentence-Level Content Planning and Style Specification for Neural Text Generation

Xinyu Hua, Lu Wang

Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as sepa…

cs.CL20195 cited

Argument Generation with Retrieval, Planning, and Realization

Xinyu Hua, Zhe Hu, Lu Wang

Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CAND…