Published: Sep 30, 2022
We are proud to announce that our group will be at COLING 2022 with two accepted long papers in the Main Track!
Here’s the list of the papers:
- Text-to-Text Extraction and Verbalization of Biomedical Event Graphs
- NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs
by G. Frisoni, G. Moro and L. Balzani
We will present our works on Event Extraction, Graph Verbalization, and Natural Language Generation Evaluation.
Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. Almost all contributions in the event realm orbit around semantic parsing, usually employing discriminative architectures and cumbersome multi-step pipelines limited to a small number of target interaction types. We present the first lightweight framework to solve both event extraction and event verbalization with a unified text-to-text approach, allowing us to fuse all the resources so far designed for different tasks. To this end, we present a new event graph linearization technique and release highly comprehensive event-text paired datasets, covering more than 150 event types from multiple biology subareas (English language). By streamlining parsing and generation to translations, we propose baseline transformer model results according to multiple biomedical text mining benchmarks and natural language generation metrics. Our extractive models achieve greater state-of-the-art performance than single-task competitors and show promising capabilities for the controlled generation of coherent natural language utterances from structured data.
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation
by G. Frisoni, A. Carbonaro, G. Moro, A. Zammarchi and M. Avagnano
Driven by deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years, with a ubiquitous task influence. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we introduce NLG-Metricverse—an end-to-end open-source library for NLG evaluation based on Python. Our framework provides a living collection of NLG metrics in a unified and easy-to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support to heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area.