Pith. sign in

REVIEW

AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1905.01984 v1 pith:4IKMERUI submitted 2019-05-01 cs.CL cs.AIcs.HC

classification cs.CLcs.AIcs.HC
keywords generationtextmethodsstatesummarizesurveyachievementsai-powered
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning. New technologies for text generation ranging from template-based methods to neural network-based methods emerged. Meanwhile, the research objectives have also changed from generating smooth and coherent sentences to infusing personalized traits to enrich the diversification of newly generated content. With the rapid development of text generation solutions, one comprehensive survey is urgent to summarize the achievements and track the state of the arts. In this survey paper, we present the general systematical framework, illustrate the widely utilized models and summarize the classic applications of text generation.

Discussion (0). Continue with ORCID to comment.

Pith tools