Constanza Fierro


Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich | Stella Frank | Heather Lent | Miryam de Lhoneux | Mostafa Abdou | Stephanie Brandl | Emanuele Bugliarello | Laura Cabello Piqueras | Ilias Chalkidis | Ruixiang Cui | Constanza Fierro | Katerina Margatina | Phillip Rust | Anders Søgaard
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.

Factual Consistency of Multilingual Pretrained Language Models
Constanza Fierro | Anders Søgaard
Findings of the Association for Computational Linguistics: ACL 2022

Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-the-blank differently for paraphrases describing the same fact. In this paper, we extend the analysis of consistency to a multilingual setting. We introduce a resource, mParaRel, and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts;and (ii) if such models are equally consistent across languages.We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages.


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200K+ Crowdsourced Political Arguments for a New Chilean Constitution
Constanza Fierro | Claudio Fuentes | Jorge Pérez | Mauricio Quezada
Proceedings of the 4th Workshop on Argument Mining

In this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods with neural network architectures. The experiments show that some of the manual tasks are suitable for automatization. In particular, the best methods achieve a 90% top-5 accuracy in a multi-class classification of arguments, and 65% macro-averaged F1-score for tagging arguments according to a three-part argumentation model.