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. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that two of the variants perform nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe.

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. LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022). The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe how we plan to use the data for modeling experiments and discuss limitations of the dataset.

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. The corpus contains 9,500 tweets annotated at the token level with codeswitches, borrowings, and named entities. This corpus differs from prior corpora of codeswitching in that we attempt to clearly define and annotate the boundary between codeswitching and borrowing and do not treat common "internet-speak" ('lol', etc.) as codeswitching when used in an otherwise monolingual context. The result is a corpus that enables the study and modeling of Spanish-English borrowing and codeswitching on Twitter in one dataset. We present baseline scores for modeling the labels of this corpus using Transformer-based language models. The annotation itself is released with a CC BY 4.0 license, while the text it applies to is distributed in compliance with the Twitter terms of service.

ParaNames: A Massively Multilingual Entity Name Corpus

This preprint describes work in progress on ParaNames, a multilingual parallel name resource consisting of names for approximately 14 million entities. The included names span over 400 languages, and almost all entities are mapped to standardized entity types (PER/LOC/ORG). Using Wikidata as a source, we create the largest resource of this type to-date. We describe our approach to filtering and standardizing the data to provide the best quality possible. ParaNames is useful for multilingual language processing, both in defining tasks for name translation/transliteration and as supplementary data for tasks such as named entity recognition and linking. We demonstrate an application of ParaNames by training a multilingual model for canonical name translation to and from English. Our resource is released at \url{https://github.com/bltlab/paranames} under a Creative Commons license (CC BY 4.0).

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. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings -- words from one language that are introduced into another without orthographic adaptation -- and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.

Toward More Meaningful Resources for Lower-resourced Languages

In this position paper, we describe our perspective on how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. We first examine two massively multilingual resources in detail. We explore the contents of the names stored in Wikidata for a few lower-resourced languages and find that many of them are not in fact in the languages they claim to be and require non-trivial effort to correct. We discuss quality issues present in WikiAnn and evaluate whether it is a useful supplement to hand annotated data. 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. We conclude with recommended guidelines for resource development.

Multilingual Open Text 1.0: Public Domain News in 44 Languages

We present a new multilingual corpus containing text in 44 languages, many of which have relatively few existing resources for natural language processing. The first release of the corpus contains over 2.7 million news articles and 1 million shorter passages published between 2001--2021, collected from Voice of America news websites. We describe our process for collecting, filtering, and processing the data. The source material is in the public domain, our collection is licensed using a creative commons license (CC BY 4.0), and all software used to create the corpus is released under the MIT License. The corpus will be regularly updated as additional documents are published.

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. In this task, we invited participants to detect lexical borrowings (coming mostly from English) in Spanish newswire texts. This task was framed as a sequence classification problem using BIO encoding. We provided participants with an annotated corpus of lexical borrowings which we split into training, development and test splits. We received submissions from 4 teams with 9 different system runs overall. The results, which range from F1 scores of 37 to 85, suggest that this is a challenging task, especially when out-of-domain or OOV words are considered, and that traditional methods informed with lexicographic information would benefit from taking advantage of current NLP trends.

Addressing Barriers to Reproducible Named Entity Recognition Evaluation

To address what we believe is a looming crisis of unreproducible evaluation for named entity recognition tasks, we present guidelines for reproducible evaluation. The guidelines we propose are extremely simple, focusing on transparency regarding how chunks are encoded and scored, but very few papers currently being published fully comply with them. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and are statistically significant. We provide SeqScore, an open source toolkit that addresses many of the issues that cause replication failures and makes following our guidelines easy.