[EMNLP 2022] Unifying and multi-tasking structured knowledge grounding with language models
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Updated
Aug 22, 2023 - Python
[EMNLP 2022] Unifying and multi-tasking structured knowledge grounding with language models
Code and Data for EMNLP2020 Paper "KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation"
SPRING is a seq2seq model for Text-to-AMR and AMR-to-Text (AAAI2021).
This repository is the official implementation of our paper MVP: Multi-task Supervised Pre-training for Natural Language Generation.
Implementation of NeurIPS 20 paper: Latent Template Induction with Gumbel-CRFs
Code for Describing a Knowledge Base
Biomedical Data-to-Text Generation via Fine-Tuning Transformers
Code for Stage-wise Fine-tuning for Graph-to-Text Generation
🧐 Code & Data for Fact-based Text Editing (Iso et al; ACL 2020)
Code for Controlling Hallucinations at Word Level in Data-to-Text Generation (C. Rebuffel, M. Roberti, L. Soulier, G. Scoutheeten, R. Cancelliere, P. Gallinari)
⛹️Code for Learning to Select, Track, and Generate for Data-to-Text (Iso et al; ACL 2019).
TCube generates rich and fluent narratives that describes the characteristics, trends, and anomalies of any time-series data (domain-agnostic) using the transfer learning capabilities of PLMs.
[COLING22] Text-to-Text Extraction and Verbalization of Biomedical Event Graphs
🏀 Script for generating the rotowire-modified dataset (Iso et al; ACL 2019)
Code for SAPPHIRE: Approaches for Enhanced Concept-to-Text Generation (https://aclanthology.org/2021.inlg-1.21/) INLG 2021 Best Long Paper.
Data-to-text generation papers
Data-to-Text generation with loosely aligned WikiBio dataset from (Lebret et al. 2016). Explicit content selection step with Multi-Instance Learning.
Code for my Master Thesis project on "Prompting Techniques for Natural Language Generation in the Medical Domain" at the University of Bologna
Codebase for the journal paper "The Rare Word Issue in Natural Language Generation: a Character-Based Solution" (Giovanni Bonetta, Marco Roberti, Rossella Cancelliere, Patrick Gallinari)
Code for IJCoL 7 Special Issue Paper - Improving Data-to-Text Generation via Preserving High-Frequency Phrases and Fact-Checking
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