[246]QANet: Towards Efficient and Human-Level Reading Comprehension on SQuAD

1. Adams Wei Yu
Deview 2018, Seoul
Quoc
Le
Thang
Luong
Rui Zhao Mohammad
Norouzi
Kai Chen
Collaborators
David Dohan

2. Bio
Adams Wei Yu
● Ph.D Candidate @ MLD, CMU
○ Advisor: Jaime Carbonell, Alex Smola
○ Large scale optimization
○ Machine reading comprehension

3. Question Answering
Concrete Answer No clear answer

4. Early Success

5. http://www.aaai.org/Magazine/Watson/watson.php
Watson: complex multi-stage system

6. Moving towards end-to-end systems
● Translation
● Question Answering

7. Lots of Datasets Available
TriviaQA
Narrative QA
MS Marco

8. Stanford Question Answer Dataset (SQuAD)
In education, teachers facilitate student learning, often in a school or
academy or perhaps in another environment such as outdoors. A teacher
who teaches on an individual basis may be described as a tutor.
Passage:
What is the role of teachers in education?Question:
facilitate student learningGroundtruth:
facilitate student learningPrediction 1: EM = 1, F1 = 1
student learningPrediction 2: EM = 0, F1 = 0.8
teachers facilitate student learningPrediction 3: EM = 0, F1 = 0.86
Data: Crowdsourced 100k question-answer pairs on 500 Wikipedia articles.

9. Roadmap
● Models for text
● General neural structures for QA
● Building blocks for QANet
○ Fully parallel (CNN + Self-attention)
○ data augmentation via back-translation
○ transfer learning from unsupervised tasks

10. That movie was awful.

11. That movie was awful .
embed embed embed embed embed
sum
Bag of words
hout

13. Continuous bag-of-words and skip-gram
architectures (Mikolov et al., 2013a;
2013b)

14. That movie was awful .
conv conv conv conv conv
sum
embed embed embed embed embed
Bag of N-Grams hout

15. That movie was awful .
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)
hinit hout
Recurrent Neural Networks

16. The quick brown fox jumped over the lazy doo

17. The quick brown fox jumped over the lazy dog

18. A feed-forward neural network language
model (Bengio et al., 2001; 2003)

19. <s> The quick brown fox
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)hinit
project
The quick brown fox jumped
Language Models

20. Yes please <de> ja bitte
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)hinit
project
Ja bitte </s>
Language Models Seq2Seq

21. Yes please <s> ja bitte
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)hinit
Ja bitte </s>
Seq2Seq + Attention
Encoder Decoder
hinit
?

22. https://distill.pub/2016/augmented-rnns/#

23. https://distill.pub/2016/augmented-rnns/#

24. Attention: a weighted average
The cat stuck out its tongue and licked its owner
The cat stuck out its tongue and licked its owner

25. Convolution:
Different linear transformations by relative position.
The cat stuck out its tongue and licked its owner
The cat stuck out its tongue and licked its owner

26. Attention: a weighted average
The cat stuck out its tongue and licked its owner
The cat stuck out its tongue and licked its owner

27. Multi-head Attention
Parallel attention layers with different linear transformations on input and output.
The cat stuck out its tongue and licked its owner
The cat stuck out its tongue and licked its owner

28. Yes please <s> Ja
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)hinit
Ja ...bitte </s>
Seq2Seq + Attention
Encoder Decoder
hinit
w1
w2

29. <s> The quick brown fox
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)
project
The quick brown fox jumped
Language Models with attention

31. Roadmap
● Models for text
● General neural structures for QA
● Building blocks for QANet
○ Fully parallel (CNN + Self-attention)
○ data augmentation via back-translation
○ transfer learning from unsupervised tasks

32. General (Doc, Question) → Answer Model

33. General framework neural QA Systems
Bi-directional Attention Flow (BiDAF)
[Seo et al., ICLR’17]

34. Base Model (BiDAF)
Similar general architectures:
● R-Net [Wang et al, ACL’17]
● DCN [Xiong et al., ICLR’17]

35. Base Model (BiDAF)
Similar general architectures:
● R-Net [Wang et al, ACL’17]
● DCN [Xiong et al., ICLR’17]

36. Base Model (BiDAF)
36
Similar general architectures:
● R-Net [Wang et al, ACL’17]
● DCN [Xiong et al., ICLR’17]

37. Base Model (BiDAF)
Similar general architectures:
● R-Net [Wang et al, ACL’17]
● DCN [Xiong et al., ICLR’17]

38. Base Model (BiDAF)
Similar general architectures:
● R-Net [Wang et al, ACL’17]
● DCN [Xiong et al., ICLR’17]

39. RNN RNN
RNN
RNN
Two Challenges with RNNs Remain...
Base Model (BiDAF)

40. First challenge: hard to capture long dependency
h1
h3
h4
h5
h6
h2
Being a long-time fan of Japanese film, I expected more than this. I can't really be
bothered to write too much, as this movie is just so poor. The story might be the cutest
romantic little something ever, pity I couldn't stand the awful acting, the mess they called
pacing, and the standard "quirky" Japanese story. If you've noticed how many Japanese
movies use characters, plots and twists that seem too "different", forcedly so, then steer
clear of this movie. Seriously, a 12-year old could have told you how this movie was
going to move along, and that's not a good thing in my book. Fans of "Beat" Takeshi: his
part in this movie is not really more than a cameo, and unless you're a rabid fan, you
don't need to suffer through this waste of film.

41. Second challenge: hard to compute in parallel
Strictly Sequential!

42. 1. local context
input
hidden state
h1
h3
h4
h5
h6
h2
2. global interaction
3. Temporal info
What do RNNs Capture?
Substitution?

43. Roadmap
● Models for text
● General neural structures for QA
● Building blocks for QANet
○ Fully parallel (CNN + Self-attention)
○ data augmentation via back-translation
○ transfer learning from unsupervised tasks

44. Convolution: Capturing Local Context
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
The todayniceisweather

45. Convolution: Capturing Local Context
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.4 0.72.51.10.6
k = 2
d = 3
0.0
0.0
0.0

46. Convolution: Capturing Local Context
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.4 0.72.51.10.6
k = 2
d = 3
0.0
0.0
0.0

47. Convolution: Capturing Local Context
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.4 0.72.51.10.6
k = 2
d = 3
0.0
0.0
0.0

48. Convolution: Capturing Local Context
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.4 0.72.51.10.6
1.8 0.90.30.41.6
k = 3k = 2
d = 3
1.2 0.81.40.52.1
k = 3
0.0
0.0
0.0
k-gram features
Fully parallel!

49. How about Global Interaction?
The todayniceisweather
layer 1
layer 2
layer 3
1. May need O(logk
N) layers
2. Interaction may become weaker
N: Seq length.
k: Filter size.

50. The todayniceisweather
The todayniceisweather
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
The todayniceisweather
w1
x + w2
x + w3
x + w4
x + w5
x
1.8
2.3
0.4
The
=
w1 w2
w3
w4
w5
w1
, w2
, w3
, w4
, w5
= softmax ( )
0.6
0.2
0.8
0.4
0.1
0.6
0.4
0.1
0.4
0.9
0.1
0.8
0.2
0.3
0.1
0.6 0.2 0.8 x
The
The todayniceisweather
[Vaswani et al., NIPS’17]

51. The todayniceisweather
The todayniceisweather
Self-attention is fully parallel & all-to-all!

52. Per Unit Total
Per Layer
Sequential Op
(Path Memory)
Self-Attn O(Nd) O(N2
d) O(1)
Conv O(kd2
) O(kNd2
) O(1)
RNN O(d2
) O(Nd2
) O(N)
Complexity
Self-Attn
Conv
RNN
N: Seq length.
d: Dim. (N > d)
k: Filter size.

53. Explicitly Encode Temporal Info
+ ++++
RNN
Position
Embedding
1 5432
1 5432
Implicit encode
explicit encode

54. Position Emb
Feedforward
Layer Norm
Self Attention
Layer Norm
Convolution
Layer Norm
+
+
+
Repeat
Position Emb
Feedforward
Self Attention
Repeat
Convolution
if you want to
go deeper
QANet Encoder
[Yu et al., ICLR’18]

55. RNN RNN
RNN
RNN
Base Model (BiDAF) → QANet
QANet Encoder QANet Encoder
QANet Encoder
QANet Encoder

56. 130
layers
QANet – 130+ layers (Deepest NLP NN)

57. QANet – First QA system with No Recurrence
● Very fast!
○ Training: 3x - 13x
○ Inference: 4x - 9x

58. QANet – 130+ layers (Deepest NLP NN)
● Layer normalization
● Residual connections
● L2
regularization
● Stochastic Depth
● Squeeze and Excitation
● ...

59. Roadmap
● Models for text
● General neural structures for QA
● Building blocks for QANet
○ Fully parallel (CNN + Self-attention)
○ data augmentation via back-translation
○ transfer learning from unsupervised tasks

60. Data augmentation: popular in vision & speech

61. More data with NMT back-translation
Input
Paraphrase
Translation
English → French
English ← French
Previously, tea had been used primarily for
Buddhist monks to stay awake during meditation.
Autrefois, le thé avait
été utilisé surtout pour
les moines bouddhistes
pour rester éveillé
pendant la méditation.
In the past, tea was used mostly for Buddhist
monks to stay awake during the meditation.

62. More data with NMT back-translation
Input
Paraphrase
Translation
English → French
English ← French
Previously, tea had been used primarily for
Buddhist monks to stay awake during meditation.
In the past, tea was used mostly for Buddhist
monks to stay awake during the meditation.
● More data
○ (Input, label)
○ (Paraphrase, label)
Applicable to virtually any NLP tasks!

63. QANet augmentation
Input
Paraphrase
Translation
English → French
English ← French
Improvement: +1.1 F1
Use 2 language pairs: English-French, English-German. 3x data.

64. Roadmap
● Models for text
● General neural structures for QA
● Building blocks for QANet
○ Fully parallel (CNN + Self-attention)
○ data augmentation via back-translation
○ transfer learning from unsupervised tasks

65. Proprietary + Confidential

67. Transfer learning for richer presentation

68. <s> The quick brown fox
embed embed embed embed embed
f(x,h) f(x,h) f(x,h) f(x,h) f(x,h)hinit
project
The quick brown fox jumped
Language Models

69. Sebastian Ruder @ Indaba 2018

70. Transfer learning for richer presentation
● Pretrained language model
(ELMo, [Peters et al., NAACL’18])
○ + 4.0 F1

71. Transfer learning for richer presentation
71
● Pretrained language model
(ELMo, [Peters et al., NAACL’18])
○ + 4.0 F1
● Pretrained machine translation
model (CoVe [McCann, NIPS’17])
○ + 0.3 F1

72. QANet – 3 key ideas
● Deep Architecture without RNN
○ 130-layer (Deepest in NLP)
● Transfer Learning
○ leverage unlabeled data
● Data Augmentation
○ with back-translation
#1 on SQuAD (Mar-Aug 2018)

73. QA is not Solved!!

74. QA is not Solved!!
Thank you!