Online

By B. Koerber. 2014

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In Reinforcement Learning

Citations in the corpus

TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “…world exhibit an astonishing breadth of linguistic phenomena used to express meaning; the World Atlas of Language Structures (Comrie and Gil, 2005; Dryer and Haspelmath, 2013) categorizes over 2,600 languages 1 by 192 typological features including phenomena such as word order, reduplication,…”
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022, background): “A second challenge for detecting subtle toxicity about minority groups is that minority mentions are more often the targets of social biases and toxicity (Hudson, 2017).”
The State and Fate of Linguistic Diversity and Inclusion in the NLP World (2020, background): “Large-scale efforts have led to the creation of a database of typological features (Dryer and Haspelmath, 2013).”
Assessing gender bias in machine translation: a case study with Google Translate (2018, background): “Also, in order to solidify our results, we have decided to work with a fair amount of gender neutral languages, forming a list of these with help from the World Atlas of Language Structures (WALS) [13] and other sources.”
Learning Language Representations for Typology Prediction (2017, background): “Typological information from sources like the World Atlas of Language Structures (WALS) (Dryer and Haspelmath, 2013), has proven useful in many NLP tasks (O’Horan et al., 2016), such as multilingual dependency parsing (Ammar et al., 2016), generative parsing in low-resource settings (Naseem et al., ”
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning (2016, methodology): “These feature vectors are derived from data from the WALS (Dryer and Haspelmath, 2013), PHOIBLE (Moran et al., 2014), and Ethno-logue (Lewis et al., 2015) typological databases via extensive post-processing and analysis.”
Earth Mover’s Distance Minimization for Unsupervised Bilingual Lexicon Induction (2017, methodology): “The typology dissimilarity is computed from features in the WALS database (Dryer and Haspelmath, 2013).”
Unsupervised Single-Scene Semantic Segmentation for Earth Observation (2022, background): “Automatic analysis of such images is useful to study various anthropogenic and natural factors, including urban monitoring [3], disaster management [4], [5], agricultural monitoring [6], and monitoring natural resources’ exploitation [7].”