Cross-lingual Name Tagging and Linking for 282 Languages

By Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, Heng Ji. 2017

578 citations recorded by the source corpus.

The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating “silver-standard” annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Citations in the corpus

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2020, background): “487 7, and 11 languages respectively; the Named Entity Recognition (NER) dataset of WikiAnn (Pan et al., 2017) restricted to the 40 languages from XTREME (Hu et al.”
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization (2020, methodology): “NER For NER, we use the Wikiann (Pan et al., 2017) dataset.”
MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer (2020, methodology): “We compare our framework against state-of-the-art cross-lingual transfer methods on the standard WikiANN NER dataset (Pan et al., 2017; Rahimi et al., 2019), relying on a representative set of ty-pologically diverse languages which includes high-resource, low-resource, as well as languages un-seen…”
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (2020, background): “, 2018; Artetxe and Schwenk, 2019), syntactic prediction (Nivre et al., 2018; Pan et al., 2017), and comprehensive benchmarks covering multiple tasks (Hu et al.”
Rethinking embedding coupling in pre-trained language models (2020, methodology): “Tasks For our experiments, we employ tasks from the XTREME benchmark (Hu et al., 2020) that require ﬁne-tuning, including the XNLI (Conneau et al., 2018), NER (Pan et al., 2017), PAWS-X (Yang et al., 2019), XQuAD (Artetxe et al., 2020), MLQA (Lewis et al., 2020), and TyDiQA-GoldP (Clark et al., 2020”
Emerging Cross-lingual Structure in Pretrained Language Models (2019, methodology): “NER We use WikiAnn (Pan et al., 2017), a silver NER dataset built automatically from Wikipedia, for English-Russian and English-French.”
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts (2020, methodology): “For NER, we use the WikiAnn (Pan et al., 2017) dataset, partitioned into train, development, and test portions by Rahimi et al. (2019).”
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models (2020, methodology): “We rely on the following NER datasets: CoNLL-2003 (Tjong Kim Sang and De Meulder, 2003), FiNER (Ruokolainen et al., 2020), Chinese Literature (Xu et al., 2017), KMOU NER29, WikiAnn (Pan et al., 2017; Rahimi et al., 2019).”
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling (2020, methodology): “• WikiAnn NER (Pan et al., 2017): The dataset contains silver standard NER tags that are annotated automatically on 282 languages that exist in Wikipedia.”
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021, methodology): “It consists of 9 tasks covering 40 diverse languages, which can be grouped into 4 broad task types (see §3.1 for details): classification (XNLI, PAWS-X), structured prediction (UD-POS, WikiANN-NER), question answering (XQuAD, MLQA, TyDiQA-GoldP), and retrieval (Tatoeba, BUCC).”
Power Hungry Processing: Watts Driving the Cost of AI Deployment? (2023, unknown): “…SD Prompts [46] IMDB [32] Rotten Tomatoes [39] SST 2 [48] maskedlanguagemodeling BookCorpus [59] C4 [42] OSCAR [37] WikiText [33] BookCorpus [59] OSCAR [37] Visual Genome [24] CPPE-5 [9] COCO [29] ReCoRD [53] WikiANN [38] CoNLL 2003 [50] Table 1: A list of the tasks and datasets used in our study.”
Polyglot Prompt: Multilingual Multitask PrompTraining (2022, background): “However, when multilingual datasets (e.g. PANX and XLSUM) are introduced ( PolyPrompt+Expand+PANX and PolyPrompt+Expand+XLSUM ), the overall performance drops (observed from Tab.”
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation (2022, background): “…(Conneau et al., 2018; Yang et al., 2019), extractive QA (Artetxe et al., 2020; Lewis et al., 2020; Clark et al., 2020), or sequence labeling (Pan et al., 2017).1 Here, we are interested in a more challenging task of zero-shot cross-lingual generation 1We refer the interested reader to…”
Massively Multilingual Transfer for NER (2019, methodology): “Our primarily evaluation is over a subset of the Wikiann NER corpus (Pan et al., 2017), using 41 out of 282 languages, where the langauges were chosen based on their overlap with multi-lingual word embedding resources from Lample et al. (2018).”
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding (2020, methodology): “Pan et al. (2017) introduced cross-lingual name tagging and linking for 282 languages.”