Lune

EMNLP2021Top-tier venue

Evaluating the Morphosyntactic Well-formedness of Generated Texts

Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov

2021Year
6Citations
2Top-tier citations

Abstract

Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -a metric to evaluate the morphosyntactic wellformedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages. 1

Ask about this paper

Your agent reads all of it.

Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.

Questions to start from

Your agent calls

Luneget_paper_fulltext

Ask in Lune

Free to start. No credit card required.

lune papers fulltext e07ad57f-9ecd-4aa3-8a27-d9de5acff859

Cited by top-tier papers2

Ask how each one uses it

Builds on7

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines