An official website of the United States government
Political and social scientists monitor, analyze and predict political unrest and violence, preventing (or mitigating) harm, and promoting the management of global conflict. They do so using event coder systems, which extract structured representations from news articles to design forecast models and event-driven continuous monitoring systems. Existing methods rely on expensive manual annotated dictionaries and do not support multilingual settings. To advance the global conflict management, we propose a novel model, Multi-CoPED (Multilingual Multi-Task Learning BERT for Coding Political Event Data), by exploiting multi-task learning and state-of-the-art language models for coding multilingual political events. This eliminates the need for expensive dictionaries by leveraging BERT models' contextual knowledge through transfer learning. The multilingual experiments demonstrate the superiority of Multi-CoPED over existing event coders, improving the absolute macro-averaged F1-scores by 23.3% and 30.7% for coding events in English and Spanish corpus, respectively. We believe that such expressive performance improvements can help to reduce harms to people at risk of violence.
more »
« less
3M-Transformers for Event Coding on Organized Crime Domain
Political scientists and security agencies increasingly rely on computerized event data generation to track conflict processes and violence around the world. However, most of these approaches rely on pattern-matching techniques constrained by large dictionaries that are too costly to develop, update, or expand to emerging domains or additional languages. In this paper, we provide an effective solution to those challenges. Here we develop the 3M-Transformers (Multilingual, Multi-label, Multitask) approach for Event Coding from domain specific multilingual corpora, dispensing external large repositories for such task, and expanding the substantive focus of analysis to organized crime, an emerging concern for security research. Our results indicate that our 3M-Transformers configurations outperform state-of-the-art usual Transformers models (BERT and XLM-RoBERTa) for coding events on actors, actions and locations in English, Spanish, and Portuguese languages.
more »
« less
- Award ID(s):
- 1931541
- PAR ID:
- 10470307
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 978-1-6654-2099-0
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Location:
- Porto, Portugal
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.more » « less
-
Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language1. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging.more » « less
-
For many years now, modern software is known to be developed in multiple languages (hence termed asmultilingualormulti-languagesoftware). Yet, to date, we still only have very limited knowledge about how multilingual software systems are constructed. For instance, it is not yet really clear how different languages are used, selected together, and why they have been so in multilingual software development. Given the fact that using multiple languages in a single software project has become a norm, understanding language use and selection (i.e.,language profile) as a basic element of themultilingual constructionin contemporary software engineering is an essential first step. In this article, we set out to fill this gap with a large-scale characterization study on language use and selection in open-source multilingual software. We start with presentingan updated overviewof language use in 7,113 GitHub projects spanning the 5 past years by characterizing overall statistics of language profiles, followed bya deeper lookinto the functionality relevance/justification of language selection in these projects through association rule mining. We proceed with an evolutionary characterization of 1,000 GitHub projects for each of the 10 past years to providea longitudinal viewof how language use and selection have changed over the years, as well as how the association between functionality and language selection has been evolving. Among many other findings, our study revealed a growing trend of using three to five languages in one multilingual software project and the noticeable stableness of top language selections. We found a non-trivial association between language selection and certain functionality domains, which was less stable than that with individual languages over time. In a historical context, we also have observed major shifts in these characteristics of multilingual systems both in contrast to earlier peer studies and along the evolutionary timeline. Our findings offer essential knowledge on the multilingual construction in modern software development. Based on our results, we also provide insights and actionable suggestions for both researchers and developers of multilingual systems.more » « less
-
null (Ed.)Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we-9*6 propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5th the training parameters compared to other word embedding methods.more » « less
