Papers with generation of natural language

2 papers
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)

Copied to clipboard

Challenge: Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming.
Approach: They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents.
Outcome: The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system .
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data (L18-1)

Copied to clipboard

Challenge: Existing approaches to generate natural language from RDF data have been proposed to generate texts in Brazilian Portuguese.
Approach: They propose a rule-based approach to verbalize RDF data to Brazilian Portuguese language.
Outcome: The proposed approach generates text similar to that generated by humans and can hence be easily understood.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations