Adversarial Examples for Reading Comprehension Evaluation

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Dive into the world of adversarial examples used to evaluate reading comprehension systems, with a focus on understanding language and human performance. Explore how models like BiDAF Ensemble are tested with adversarial questions and see the impact of adding grammatical sentences to improve system performance.

  • Adversarial Examples
  • Reading Comprehension
  • Language Understanding
  • Evaluation
  • BiDAF Ensemble

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  1. Adversarial Examples for Evaluating Reading Comprehension Systems Robin Jia and Percy Liang Stanford University

  2. Reading Comprehension Task Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689 but quite a few arrived as late as 1700; thereafter, the numbers declined Correct Answer: 1700 Stanford Question Answering Dataset (Rajpurkar et al., 2016) 2

  3. Progress on SQuAD Human Performance Do these models actually understand language? Logistic Regression Baseline SQuAD leaderboard, https://rajpurkar.github.io/SQuAD-explorer/ 3

  4. Adversarial Evaluation Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689 but quite a few arrived as late as 1700; thereafter, the numbers declined. The number of old Acadian colonists declined after the year of 1675. Correct Answer: 1700 Predicted Answer: 1675 Model used: BiDAF Ensemble (Seo et al., 2016) 4

  5. Adversarial Evaluation Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689 but quite a few arrived as late as 1700; thereafter, the numbers declined. expected yet later be basis need young only required 1961. Correct Answer: 1700 Predicted Answer: 1961 Model used: BiDAF Ensemble (Seo et al., 2016) 5

  6. Outline Inspiration/Motivation Adding Grammatical Sentences Adding Word Salad Trying to build better systems 6

  7. Outline Inspiration/Motivation Adding Grammatical Sentences Adding Word Salad Trying to build better systems 7

  8. Some Inspiration + .007 * = Panda 58% confidence Nematode 8% confidence Gibbon 99% confidence Local perturbations don t change semantics of image, but models are oversensitive to small differences! Goodfellow et al., 2014. 8

  9. Local perturbations of text Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689 but quite a few arrived as late as 1700; thereafter, the numbers amount declined decreased Plausible alternative answers not always present Hard to find a lot of perturbations to try Li et al., 2017 9

  10. Preserving Semantics For images, most local perturbations preserve semantics For text, most local perturbations alter semantics Even changing one word by a small amount may not preserve semantics (e.g. entity names) 10

  11. Concatenative Adversaries Instead of locally altering the input, append distracting text to the paragraph Must ensure that added text does not actually answer the question 11

  12. Distracting Text Question: The number of new Huguenot colonists declined after what year ? Distracting text: The number of new Huguenot colonists declined after the year 1675 . Answer according to text: 1675 12

  13. Distracting Text Question: The number of new Huguenot colonists declined after what year ? Distracting text: The number of new old Huguenot Acadian colonists declined after the year 1675 . Answer according to text: N/A Local perturbations change semantics of sentence, but models are overly stable/insensitive to these changes! 13

  14. Outline Inspiration/Motivation Adding Grammatical Sentences Adding Word Salad Trying to build better systems 14

  15. Grammatical Distractors What city did Tesla move to in 1880? Change entities, numbers, antonyms Prague Generate fake answer with same NER/POS tag What city did Tadakatsu move to in 1881? Chicago Convert to declarative sentence Tadakatsu moved the city of Chicago to in 1881. Have crowdworkers fix errors Tadakatsu moved to the city of Chicago in 1881. 15

  16. Four dev systems SQuAD leaderboard, https://rajpurkar.github.io/SQuAD-explorer/ *Some of our results are on older versions of models than shown here 16

  17. Results (4 dev systems) System Original 80.0 75.5 75.4 71.4 92.6 AddOneSent 46.9 45.7 41.8 39.0 89.2 BiDAF, ensemble (Seo et al., 2016) BiDAF, single (Seo et al., 2016) Match-LSTM, ensemble (Wang & Jiang, 2016) Match-LSTM, single (Wang & Jiang, 2016) Human Performance 17

  18. Picking a worst-case sentence Tadakatsu moved the city of Chicago to in 1881. Have crowdworkers fix errors Tadakatsu moved to the city of Chicago in 1881. Tadakatsu moved to Chicago in 1881. In 1881, Tadakatsu moved to the city of Chicago. Model failed if distracted by any of these 18

  19. Results (4 dev systems) System Original 80.0 75.5 75.4 71.4 92.6 AddOneSent 46.9 45.7 41.8 39.0 89.2 AddSent 34.2 34.3 29.4 27.3 79.5 BiDAF, ensemble (Seo et al., 2016) BiDAF, single (Seo et al., 2016) Match-LSTM, ensemble (Wang & Jiang, 2016) Match-LSTM, single (Wang & Jiang, 2016) Human Performance 19

  20. Computers on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph Gospi Chicago Model 20

  21. Computers on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph Gospi Chicago Model Deterministically choose argmax 21

  22. Humans on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph Gospi Chicago Crowd Only get noisy samples! 22

  23. Humans on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph Gospi Chicago Crowd Only get noisy samples! 23

  24. Humans on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph #2 Gospi Chicago Crowd Only get noisy samples! 24

  25. Humans on AddSent What city did Tesla move to in 1880? Prague Adversarial Paragraph #3 Gospi Chicago Crowd Noise augmented when picking worst-case sentence 25

  26. Twelve test systems SQuAD leaderboard, https://rajpurkar.github.io/SQuAD-explorer/ *Some of our results are on older versions of models than shown here 26

  27. Results (12 test systems) System Original 81.1 80.1 79.1 78.8 78.6 78.5 78.2 77.0 76.9 76.2 69.3 50.4 AddOneSent 49.8 46.5 55.3 47.7 47.0 56.0 50.3 50.0 44.8 49.5 45.1 30.4 AddSent 39.4 35.0 46.2 37.4 37.9 46.6 39.4 40.3 33.9 39.5 37.8 23.2 ReasoNet, ensemble (Shen et al., 2017) SEDT, ensemble (Liu et al., 2017) Mnemonic Reader, ensemble (Hu et al., 2017) Ruminating Reader (Gong and Bowman, 2017) jNet (Zhang et al., 2017) Mnemonic Reader, single (Hu et al., 2017) ReasoNet, single (Shen et al., 2017) MPCM, single (Wang et al., 2016) SEDT, single (Liu et al., 2017) RaSOR (Lee et al., 2016) DCR (Yu et al., 2016) Logistic Regression (Rajpurkar et al., 2016) 27

  28. Results (12 test systems) System Original 81.1 80.1 79.1 78.8 78.6 78.5 78.2 77.0 76.9 76.2 69.3 50.4 AddOneSent 49.8 46.5 55.3 47.7 47.0 56.0 50.3 50.0 44.8 49.5 45.1 30.4 AddSent 39.4 35.0 46.2 37.4 37.9 46.6 39.4 40.3 33.9 39.5 37.8 23.2 ReasoNet, ensemble (Shen et al., 2017) SEDT, ensemble (Liu et al., 2017) Mnemonic Reader, ensemble (Hu et al., 2017) Ruminating Reader (Gong and Bowman, 2017) jNet (Zhang et al., 2017) Mnemonic Reader, single (Hu et al., 2017) ReasoNet, single (Shen et al., 2017) MPCM, single (Wang et al., 2016) SEDT, single (Liu et al., 2017) RaSOR (Lee et al., 2016) DCR (Yu et al., 2016) Logistic Regression (Rajpurkar et al., 2016) 28

  29. Results (12 test systems) System Original 81.1 80.1 79.1 78.8 78.6 78.5 78.2 77.0 76.9 76.2 69.3 50.4 AddOneSent 49.8 46.5 55.3 47.7 47.0 56.0 50.3 50.0 44.8 49.5 45.1 30.4 AddSent 39.4 35.0 46.2 37.4 37.9 46.6 39.4 40.3 33.9 39.5 37.8 23.2 ReasoNet, ensemble (Shen et al., 2017) SEDT, ensemble (Liu et al., 2017) Mnemonic Reader, ensemble (Hu et al., 2017) Ruminating Reader (Gong and Bowman, 2017) jNet (Zhang et al., 2017) Mnemonic Reader, single (Hu et al., 2017) ReasoNet, single (Shen et al., 2017) MPCM, single (Wang et al., 2016) SEDT, single (Liu et al., 2017) RaSOR (Lee et al., 2016) DCR (Yu et al., 2016) Logistic Regression (Rajpurkar et al., 2016) 29

  30. Partial Matches Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689 but quite a few arrived as late as 1700; thereafter, the numbers declined. The number of old Acadian colonists declined after the year of 1675. All models distracted by sentences with only partial match with the question 30

  31. Partial Matches Question: The number of new Huguenot colonists declined after what year? Paragraph: The largest portion of the Huguenots to settle in the Cape arrived between 1688 and 1689, in seven ships as part of the organised migration, but quite a few arrived as late as 1700; thereafter, the numbersdeclined, and only small groups arrived at a time. Correct Answer: 1700 Stanford Question Answering Dataset (Rajpurkar et al., 2016) 31

  32. Outline Inspiration/Motivation Adding Grammatical Sentences Adding Word Salad Trying to build better systems 32

  33. Adversarial Word Salad So far, only explored tiny fraction of possible distractors Try adding any ungrammatical sequence of words Incoherent text cannot provide evidence for an incorrect answer 33

  34. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague Model predicts: Prague Model used: BiDAF Ensemble (Seo et al., 2016) 34

  35. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design theory even medical process. Add random common words 35

  36. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design theory even medical process. Pick one word at random 36

  37. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design city even medical process. Replace with another common word or question word, to increase probability that model gives a wrong answer 37

  38. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design cityeven medical process. Pick one word at random 38

  39. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries what design cityeven medical process. Replace with another common word or question word, to increase probability that model gives a wrong answer 39

  40. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague what 30 city 1880 what move city city medical move. Model predicts: medical Model used: BiDAF Ensemble (Seo et al., 2016) 40

  41. AddAny Results System Original 80.0 75.5 75.4 71.4 AddOneSent 46.9 45.7 41.8 39.0 AddSent 34.2 34.3 29.4 27.3 AddAny 2.7 4.8 11.7 7.6 BiDAF, ensemble BiDAF, single Match-LSTM, ensemble Match-LSTM, single Models can be fooled on almost any example 41

  42. Some Inspiration + .007 * = Panda 58% confidence Nematode 8% confidence Gibbon 99% confidence Goodfellow et al., 2014. 42

  43. AddAny What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague what 30 city1880whatmove citycity medical move. Model predicts: medical Model used: BiDAF Ensemble (Seo et al., 2016) 43

  44. AddCommon What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague Model predicts: Prague Model used: BiDAF Ensemble (Seo et al., 2016) 44

  45. AddCommon What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design theory even medical process. Add random common words 45

  46. AddCommon What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design theory even medical process. Pick one word at random 46

  47. AddCommon What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague heavy industry art countries applied design around even medical process. Replace with another common word, to increase probability that model gives a wrong answer 47

  48. AddCommon What city did Tesla move to in 1880? In January 1880, two of Tesla's uncles put together enough money to help him leave Gospi for Prague finally back would move york hotel through then immediately later. Model predicts: york hotel Model used: BiDAF Ensemble (Seo et al., 2016) 48

  49. AddCommon Results System Original 80.0 75.5 75.4 71.4 AddOneSent 46.9 45.7 41.8 39.0 AddSent 34.2 34.3 29.4 27.3 AddAny 2.7 4.8 11.7 7.6 AddCommon 52.6 41.7 51.0 38.9 BiDAF, ensemble BiDAF, single Match-LSTM, ensemble Match-LSTM, single 49

  50. AddCommon Errors Question: What type of markets is the dwelling type below? Distracting text: be therefore dark business business other system type feet above. Predicted Answer: dark business Model used: BiDAF Ensemble (Seo et al., 2016) 50

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