We present NN-RANK, an algorithm for ranking source languages for cross-lingual transfer, which leverages hidden representations from multilingual models and unlabeled targetlanguage data.We experiment with two pretrained multilingual models and two tasks: partof-speech tagging (POS) and named entity recognition (NER).We consider 51 source languages and evaluate on 56 and 72 target languages for POS and NER, respectively.When using in-domain data, NN-RANK beats stateof-the-art baselines that leverage lexical and linguistic features, with average improvements of up to 35.56 NDCG for POS and 18.14 NDCG for NER.As prior approaches can fall back to language-level features if target language data is not available, we show that NN-RANK remains competitive using only the Bible, an out-of-domain corpus available for a large number of languages.Ablations on the amount of unlabeled target data show that, for subsets consisting of as few as 25 examples, NN-RANK produces high-quality rankings which achieve 92.8% of the NDCG achieved using all available target data for ranking.POS test-all NER test-all

Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Abteen Ebrahimi, Katharina Kann. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive.This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output.We present YELPNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains.The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.