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The Voice of Time

  1. 1. The Voice of Time Michael Falk University of Kent and Western Sydney University
  2. 2. 1. Australia in the Pacific 2. A Romanticist’s Contribution 3. Workflow: harvest, clean, decipher
  3. 3. 1 | Australia in the Pacific Source: AFP
  4. 4. Source: Patrick Kirch, On the Road of the Winds: An Archaeological History of the Pacific Islands before European Contact (Oakland: University of California Press, 2017), p. 6.
  5. 5. Sources: Fishhook map and photo: Val Attenbrow, ‘Aboriginal fishing in Port Jackson’, in The Natural History of Sydney (Sydney, 2010); Dingo photograph: Henry Whitehead - Original photograph, CC BY-SA 3.0,; Sahul and Sundaland map: Kirch, p. 57; Linguistic data: Rachel Hendery (personal communication), and POLLEX database. Language Word Hawaiian kapu NZ Maori tapu Proto southern Vanuatu *tabur Gugu Yimidhirr thabul
  6. 6. 2 | A Romanticist’s Contribution
  7. 7. Dunlop’s transcription: Nge a runba wonung bulkirra umbilinto bulwarra; Pital burra kultan wirripang buntoa Modern reconstruction (Wafer 2017, p. 204): ngayaranpa wanang palkirr yampilintu pulwarra pital para katan wiripang pantuwa Dunlop’s poetic translation: Our home is the gibber-gunyah Where hill joins hill on high; … And the rushing of wings, as the wangas pass, Sweeps the wallaby’s print from the glistening grass. Modern literal translation (Wafer 2017, p. 206): Ours is the place where the mountains cohabit with the heights The eaglehawks and wallabies are happy TheSydneyMorningHerald,11Oct1848
  8. 8. 3 | Workflow: Harvest, Clean, Decipher © BBC
  9. 9. Harvest Clean Decipher Encoder-Decoder Text Correction Model Language Classification Model Train Train ALTA dataset of 6000 hand- corrected articles (Cassidy and Mollá, 2017) ??? InferCleaned tokensCleanTokenised text Local PostgreSQL Database Untold Riches Public API
  10. 10. Google Colab
  11. 11. RNN Basics: A single time-step RNN Cell (this time- step) i 0, 0, 0, … 1, … 0, 0, 0 ‘one- hot’ vector RNN Cell (previous time-step) 0.99 0.24 0.01 ... c<t-1>(n-1) c<t-1>(n) o<t> RNN Cell (next time- step) c<t> c<t>: the ‘memory cell’ at time-step t. It is updated each time and saved for the next time-step o<t>: the ‘output’ at time-step t. k n
  12. 12. Model design #1: A general English-language model RNN Cell k RNN Cell i RNN Cell n RNN Cell d RNN Cell y RNN Cell n RNN Cell e RNN Cell E RNN Cell S σ σ σ σ σ σ σ σ σ σ The ‘softmax activation function’ guesses the next letter based on the output of the cell, returning a vector of probabilities, e.g.: (P(a)=0.01, P(b)=0.4, P(c)=0.02, … P(z)=0.001, P(S)=0.1, P(E)=0.002)
  13. 13. The Problem
  14. 14. Model design #2: Binary classification model RNN Cell S The ‘softmax activation function’ predicts whether the whole word is English or Australian and simply outputs a two-vector, e.g.: (P(English)=0.37, P(Australian)=0.63) RNN Cell RNN Cell k RNN Cell RNN Cell i RNN Cell RNN Cell n RNN Cell RNN Cell e RNN Cell RNN Cell E RNN Cell … … … Concatenate σ
  15. 15. Problems and Promises

Editor's Notes

  • Kiribati, Cook Islands, Tonga and Solomon Islands
  • Green = training set, blue = validation set
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