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Found 11 papers, 0 papers with code
Sequentially Controlled Text Generation
While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure.
Efficient Argument Structure Extraction with Transfer Learning and Active Learning
Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation
To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims.
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation
In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon a large model, BART.
XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference
We therefore propose a novel model, XREF, that leverages attention mechanisms to (1) pinpoint relevant context within comments, and (2) detect supporting entities from the news article.
Sentence-Level Content Planning and Style Specification for Neural Text Generation
Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems.
Argument Generation with Retrieval, Planning, and Realization
Automatic argument generation is an appealing but challenging task.
Argument Mining for Understanding Peer Reviews
Peer-review plays a critical role in the scientific writing and publication ecosystem.
Neural Argument Generation Augmented with Externally Retrieved Evidence
High quality arguments are essential elements for human reasoning and decision-making processes.
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
We study the problem of domain adaptation for neural abstractive summarization.
Understanding and Detecting Supporting Arguments of Diverse Types
We investigate the problem of sentence-level supporting argument detection from relevant documents for user-specified claims.
