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Found 19 papers, 9 papers with code
MasakhaNER: Named Entity Recognition for African Languages
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders.
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
Multilingual Open Text Release 1: Public Domain News in 44 Languages
We present Multilingual Open Text (MOT), a new multilingual corpus containing text in 44 languages, many of which have limited existing text resources for natural language processing.
ParaNames: A Massively Multilingual Entity Name Corpus
We demonstrate an application of ParaNames by training a multilingual model for canonical name translation to and from English.
SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation
To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility.
Mining Wikidata for Name Resources for African Languages
This work supports further development of language technology for the languages of Africa by providing a Wikidata-derived resource of name lists corresponding to common entity types (person, location, and organization).
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling
This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task.
Macro-Average: Rare Types Are Important Too
While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy.
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval
When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).
The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation
This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting.
TMR: Evaluating NER Recall on Tough Mentions
We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of "tough" mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data.
Overview of ADoBo 2021: Automatic Detection of Unassimilated Borrowings in the Spanish Press
This paper summarizes the main findings of the ADoBo 2021 shared task, proposed in the context of IberLef 2021.
If You Build Your Own NER Scorer, Non-replicable Results Will Come
We propose best practices to increase the replicability of NER evaluations by increasing transparency regarding the handling of improper label sequences.
Effective Architectures for Low Resource Multilingual Named Entity Transliteration
In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English.
Toward More Meaningful Resources for Lower-resourced Languages
We then discuss the importance of creating annotation for lower-resourced languages in a thoughtful and ethical way that includes the languages' speakers as part of the development process.
Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing
We present a new corpus of Twitter data annotated for codeswitching and borrowing between Spanish and English.
LR-Sum: Summarization for Less-Resourced Languages
This preprint describes work in progress on LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.
What changes when you randomly choose BPE merge operations? Not much
We introduce three simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task.
