mlsanityData to 7 Oct 2026

Measuring Linguistic Diversity During COVID-19

arXiv 2104.01290 ↗First posted 3 Apr 2021cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, however, has been to describe these corpora themselves rather than to make inferences about underlying populations. This paper shows that a difference-in-differences method based on the Herfindahl-Hirschman Index can identify the bias in digital corpora that is introduced by non-local populations. These methods tell us where significant changes have taken place and whether this leads to increased or decreased diversity. This is an important step in aligning digital corpora like social media with the real-world populations that have produced them.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 41 of its 111 references that are in the map. First posted on arXiv 3 Apr 2021.

Builds on

41 of 111 references.

References · 41

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  7. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  8. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  9. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  10. 2018Self-Attention with Relative Position Representationscited by 1,112
  11. 2018Self-Attention Generative Adversarial Networkscited by 809
  12. 2018Image Transformercited by 770
29 more
  1. 2017Convolutional Sequence to Sequence Learningcited by 550
  2. 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
  3. 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 305
  4. 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
  5. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  6. 2016Long Short-Term Memory-Networks for Machine Readingcited by 161
  7. 2016Generating Visual Explanationscited by 156
  8. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  9. 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
  10. 2017DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understandingcited by 71
  11. 2018Linguistically-Informed Self-Attention for Semantic Role Labelingcited by 71
  12. 2018Faithful Multimodal Explanation for Visual Question Answeringcited by 64
  13. 2019TEQUILA: Temporal Question Answering over Knowledge Basescited by 47
  14. 2020Mapping Languages: The Corpus of Global Language Usecited by 42
  15. 2018Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captionscited by 22
  16. 2019SemEval-2013 Task 2: Sentiment Analysis in Twittercited by 22
  17. 2016The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Paperscited by 20
  18. 2017Frustratingly Short Attention Spans in Neural Language Modelingcited by 18
  19. 2018SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approachcited by 18
  20. 2018Explaining Deep Learning Models - A Bayesian Non-parametric Approachcited by 16
  21. 2019Modeling Global Syntactic Variation in English Using Dialect Classificationcited by 13
  22. 2020Template-Based Question Answering over Linked Geospatial Datacited by 12
  23. 2013Where in the World are You? Geolocation and Language Identification in Twittercited by 11
  24. 2014Crowdsourcing Dialect Characterization through Twittercited by 11
  25. 2017Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasetscited by 8
  26. 2017Structural Attention Neural Networks for improved sentiment analysiscited by 6
  27. 2020Mapping Languages and Demographics with Georeferenced Corporacited by 6
  28. 2016Immigrant community integration in world citiescited by 3
  29. 2019Conditioning LSTM Decoder and Bi-directional Attention Based Question Answering Systemcited by 1

Built on by

Not cited yet.