Download as PDF, PPTX
![Derivation of ELBO
9
We then use variational posteriors to maximize following Evidence Lower Bound:
log 𝑝! 𝑥, 𝑦 𝑐 ≥ 𝔼"!~#$ 𝑧# 𝑥, 𝑐 [log 𝑝! 𝑥 𝑧#, 𝑦, 𝑐 ]
+ 𝔼"%~%$ 𝑧& 𝑧#, 𝑦, 𝑐 log 𝑝! 𝑦 𝑧&, 𝑐
−𝐷'([𝑞) 𝑧& 𝑧#, 𝑦, 𝑐 ||𝑝*(𝑧&|𝑧#, 𝑐)]
−𝐷'([𝑞) 𝑧# 𝑥, 𝑐 ||𝑝* 𝑧# 𝑐 ]
=: ℒ+,-./](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-9-320.jpg)
![Hierarchical Conditional VAEs
13
AG network aims at generating answer spans from categorical L.V:
At the end of the main drive,
is a simple, modern stone
statue of mary.
modern stone statue of mary
Bi-LSTM Decoder
Answer
Heuristic Matching
Answer Generation
Heuristic Matching Layer in NLI [Mou2016],
Bi-LSTM,
2 linear layers to predict answer spans
[Mou2016] Lili Mou et. al., Natural language inference by tree-based convolution and heuristic matching, ACL 2016
(b) Answer Generation Network](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-13-320.jpg)
![Hierarchical Conditional VAEs
14
QG network aims at generating question from answer and continuous L.V:
Question Generation
Bi-LSTM with gated self-attention [Wang2017],
Luong attention [Luong2015],
LSTM,
Linear layer to predict words,
Maxout copy mechanism [Zhao2018]
what is at the end of
the main drive?
At the end of the main drive,
is a simple, modern stone
statue of mary.
copy
Bi-LSTM
Encoder
Attention
LSTM
Decoder
Question
(c) Question Generation Network
[Wang2017] Wenhui Wang et. al., Gated self-matching net-works for reading comprehension and question answering, ACL 2017
[Luong2015] Thang Luong et. al. Effective approaches to attention-based neural machine translation, EMNLP 2015,
[Zhao2018] Yao Zhao el. al. Paragraph-level neural question generation with maxout pointer and gated self-attention networks, EMNLP2018](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-14-320.jpg)
![Mutual Information Maximization
15
Semantic consistency of QA pairs is important.
Context: ...during the age of enlightenment, philosophers such as
john locke advocated the principle in their writings, whereas others,
such as thomas hobbes, strongly opposed it.
Ground Truth: who was an advocate of separation of powers?
Generated: who opposed the principle of enlightenment?
[Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-15-320.jpg)
![Mutual Information Maximization
16
Maximizing the mutual information of QA pairs: Intractable
[Belghazi 2018] Belghazi et al., MINE: Mutual Information Neural Estimation, ICML2018
𝐼 𝑋; 𝑌 = 𝐷!"(ℙ#$||ℙ#⨂ℙ$)
= ∫𝒳×𝒴
log
(ℙ!"
(ℙ!⨂ℙ"
𝑑ℙ#$
≥ 𝔼#$ 𝑇+(𝑥, 𝑦 ] − log 𝔼#⨂,$[exp(𝑇+ 𝑥, 𝑦 ]
𝑇+ ∶ 𝒳 × 𝒴 → ℝ is a discriminator](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-16-320.jpg)
![Mutual Information Maximization
17
Alternative estimator: Jensen-Shannon divergence
[Hjelm 2019] Hjelm et al., Learning deep representations by mutual information estimation and maximization, ICLR 2019
𝑀𝐼 𝑋, 𝑌 ≥ 𝔼!"[log 𝜎(𝑇# 𝑥, 𝑦 )]
+𝔼!⨂"[log 1 − 𝜎 𝑇# 𝑥%
, 𝑦 ]
𝜎 𝑥 =
&
&'()*(,-)](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-17-320.jpg)
![Average pooling of hidden states
𝑀𝐼 𝑋, 𝑌 ≥ 𝔼-,0~ℙ[log 𝑔(𝑥, 𝑦)]
+
&
3
𝔼4-,0~ℕ[log(1 − 𝑔 8𝑥, 𝑦 )]
+
&
3
𝔼-, 40~ℕ[log(1 − 𝑔 𝑥, 8𝑦 )]
=: ℒ6789
𝑔(𝑥, 𝑦) = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑( ̅𝑥:
𝑊 B𝑦)
Mutual Information Maximization
18
Following Yeh and Chen (2019), we maximize MI of QA pairs as follows:
[Yeh 2019] Yeh and Chen, QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization EMNLP 2019
Negative Examples](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-18-320.jpg)
![Experimental Setup
21
3) Baselines
• Harvesting-QG [Du 2018]:
Seq2Seq + attention + copy
• Maxout-QG [Zhao 2018]:
Seq2Seq + attention + Maxout copy + BERT encoder
• Semantic-QG [Zhang 2019]:
Seq2Seq + attention + Maxout copy + BERT encoder + Reinforcement
[Du 2018] Du and Cardie, Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia , NAACL2018
[Zhao2018] Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks, EMNLP 2018
[Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-21-320.jpg)
![QA-based Evaluation (QAE)
22
Train QA model with synthetic data and measure F1, EM with annotated data.
Synthetic data Annotated data
Train Test
[Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019](https://image.slidesharecdn.com/generatingdiverseandconsistentqapairsfromcontextswithinformation-maximizinghierarchicalconditionalva-200723081242/85/Generating-Diverse-and-Consistent-QA-pairs-from-Contexts-with-Information-Maximizing-Hierarchical-Conditional-VAEs-22-320.jpg)
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Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
- 1. Generating Diverse andConsistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs Dong Bok Lee1*, Seanie Lee1*, Woo Tae Jung2 , Donghwan Kim2 and Sung Ju Hwang1,3 KAIST1, Daejeon, South Korea 42MARU2, Seoul, South Korea AITRICS3, Seoul, South Korea 1
- 2. Data Scarcity inQuestion Answering (QA) 2 One of the most crucial challenges in QA is the scarcity of labeled data. Since it is costly to obtain QA pairs for target text domain with human annotation. Human AnnotatorNew domain Coronavirus (COVID-19) Coronaviruses are a group of related RNA viruses that cause diseases in mammals and birds. In humans, these viruses cause respiratory tract infections that can range from mild to lethal. Mild illnesses include some cases of the common cold (which is also caused by other … QA Annotation Q: When did the COVID-19 occur? A: 2019
- 3. Neural Question Generation(NQG) 3 Neural Question (and answer) generation is a popular approach to overcome this. Neural Networks Input (paragraph) Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals Q1: which city has more murals than any other city? A1: Philadelphia Q2: why Philadelphia has more murals? A2: the 1984 creation of the department of recreation’s mural arts program Q3: when did the department of recreation’s mural arts program start? A3: 1984 Q4: how many murals funded the graffiti arts program by the department of recreation? A4: more than 2,800 Seq2Seq + attention + copy
- 4. Diversity: One-to-many Problem 4 Existingsystems have overlooked that QAG is essentially a one-to-many problem. Neural Networks Input (paragraph) Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals Q1: which city has more murals than any other city? A1: Philadelphia Q2: why Philadelphia has more murals? A2: the 1984 creation of the department of recreation’s mural arts program Q3: when did the department of recreation’s mural arts program start? A3: 1984 Q4: how many murals funded the graffiti arts program by the department of recreation? A4: more than 2,800 Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals Q1: which city has more murals than any other city? A1: Philadelphia Q2: why Philadelphia has more murals? A2: the 1984 creation of the department of recreation’s mural arts program Q3: when did the department of recreation’s mural arts program start? A3: 1984 Q4: how many murals funded the graffiti arts program by the department of recreation? A4: more than 2,800
- 5. Consistency: Answerability ofQuestion 5 No constraint for consistency between question and answer. Neural Networks Input (paragraph) Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals Q1: which city has more murals than any other city? A1: Philadelphia Q2: why Philadelphia has more murals? A2: the 1984 creation of the department of recreation’s mural arts program Q3: when did the department of recreation’s mural arts program start? A3: 1984 Q4: how many murals funded the graffiti arts program by the department of recreation? A4: more than 2,800
- 6. Info-Maximizing Hierarchical ConditionalVAEs 6 To overcome these challenges, we propose Info-HCVAE for QA pairs generation. Diversity-> deep latent variable model (HCVAE) Consistency-> Mutual Information Maximization +
- 7. Derivation of ELBO 7 Formally,our goal is to learn conditional joint distribution as follows: Question which city has more murals than any other city? Answer Philadelphia Context (paragraph) Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals 𝑥, 𝑦 ~ 𝑝(𝑥, 𝑦|𝑐)
- 8. Derivation of ELBO 8 Inhere, we introduce a separate latent space for question and answer as follows: Discrete R.V. (e.g., Categorical Distribution) Continuous R.V. (e.g., Gaussian Distribution) 𝑝 𝑥, 𝑦 𝑐 = ' !! ( !" 𝑝 𝑥 𝑧", 𝑦, 𝑐 𝑝(𝑦|𝑧", 𝑧#, 𝑐) 𝑝 𝑧# 𝑧", 𝑐 𝑝 𝑧" 𝑐 𝑑𝑧"
- 9. Derivation of ELBO 9 Wethen use variational posteriors to maximize following Evidence Lower Bound: log 𝑝! 𝑥, 𝑦 𝑐 ≥ 𝔼"!~#$ 𝑧# 𝑥, 𝑐 [log 𝑝! 𝑥 𝑧#, 𝑦, 𝑐 ] + 𝔼"%~%$ 𝑧& 𝑧#, 𝑦, 𝑐 log 𝑝! 𝑦 𝑧&, 𝑐 −𝐷'([𝑞) 𝑧& 𝑧#, 𝑦, 𝑐 ||𝑝*(𝑧&|𝑧#, 𝑐)] −𝐷'([𝑞) 𝑧# 𝑥, 𝑐 ||𝑝* 𝑧# 𝑐 ] =: ℒ+,-./
- 10. Derivation of ELBO 10 Aftertraining, the generative process of HCVAE is as follows: 1. 𝑝! 𝑧" 𝑐 2. 𝑝! 𝑧# 𝑧", 𝑐 3. 𝑝$ 𝑦 𝑧#, 𝑐 4. 𝑝$ 𝑥 𝑧", 𝑦, 𝑐 1. Sample question L.V.: 𝑧, ~ 𝑝-(𝑧,|𝑐) 2. Sample answer L.V.: 𝑧. ~ 𝑝-(𝑧.|𝑧,, 𝑐) 3. Generate an answer: y ~ 𝑝/(𝑦|𝑧., 𝑐) 4. Generate a question: x ~ 𝑝/(𝑥|𝑧,, 𝑦, 𝑐)
- 11. Hierarchical Conditional VAEs 11 Thisis the overall description of our specific implementation for HCVAE.
- 12. Hierarchical Conditional VAEs 12 ConditionalPosterior / Prior networks aim at mapping the input into latent space. Posterior 3 Bi-LSTMs, 2 MLPs, attention Prior 1 Bi-LSTM, 1 MLP M L PContext At the end of the main drive, is a simple, modern stone statue of mary. what is at the end of the main drive? Question Answer Bi-LSTM Encoder (a) Conditional Posterior / Prior Networks Bi-LSTM Encoder Bi-LSTM Encoder M L P
- 13. Hierarchical Conditional VAEs 13 AGnetwork aims at generating answer spans from categorical L.V: At the end of the main drive, is a simple, modern stone statue of mary. modern stone statue of mary Bi-LSTM Decoder Answer Heuristic Matching Answer Generation Heuristic Matching Layer in NLI [Mou2016], Bi-LSTM, 2 linear layers to predict answer spans [Mou2016] Lili Mou et. al., Natural language inference by tree-based convolution and heuristic matching, ACL 2016 (b) Answer Generation Network
- 14. Hierarchical Conditional VAEs 14 QGnetwork aims at generating question from answer and continuous L.V: Question Generation Bi-LSTM with gated self-attention [Wang2017], Luong attention [Luong2015], LSTM, Linear layer to predict words, Maxout copy mechanism [Zhao2018] what is at the end of the main drive? At the end of the main drive, is a simple, modern stone statue of mary. copy Bi-LSTM Encoder Attention LSTM Decoder Question (c) Question Generation Network [Wang2017] Wenhui Wang et. al., Gated self-matching net-works for reading comprehension and question answering, ACL 2017 [Luong2015] Thang Luong et. al. Effective approaches to attention-based neural machine translation, EMNLP 2015, [Zhao2018] Yao Zhao el. al. Paragraph-level neural question generation with maxout pointer and gated self-attention networks, EMNLP2018
- 15. Mutual Information Maximization 15 Semanticconsistency of QA pairs is important. Context: ...during the age of enlightenment, philosophers such as john locke advocated the principle in their writings, whereas others, such as thomas hobbes, strongly opposed it. Ground Truth: who was an advocate of separation of powers? Generated: who opposed the principle of enlightenment? [Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019
- 16. Mutual Information Maximization 16 Maximizingthe mutual information of QA pairs: Intractable [Belghazi 2018] Belghazi et al., MINE: Mutual Information Neural Estimation, ICML2018 𝐼 𝑋; 𝑌 = 𝐷!"(ℙ#$||ℙ#⨂ℙ$) = ∫𝒳×𝒴 log (ℙ!" (ℙ!⨂ℙ" 𝑑ℙ#$ ≥ 𝔼#$ 𝑇+(𝑥, 𝑦 ] − log 𝔼#⨂,$[exp(𝑇+ 𝑥, 𝑦 ] 𝑇+ ∶ 𝒳 × 𝒴 → ℝ is a discriminator
- 17. Mutual Information Maximization 17 Alternativeestimator: Jensen-Shannon divergence [Hjelm 2019] Hjelm et al., Learning deep representations by mutual information estimation and maximization, ICLR 2019 𝑀𝐼 𝑋, 𝑌 ≥ 𝔼!"[log 𝜎(𝑇# 𝑥, 𝑦 )] +𝔼!⨂"[log 1 − 𝜎 𝑇# 𝑥% , 𝑦 ] 𝜎 𝑥 = & &'()*(,-)
- 18. Average pooling ofhidden states 𝑀𝐼 𝑋, 𝑌 ≥ 𝔼-,0~ℙ[log 𝑔(𝑥, 𝑦)] + & 3 𝔼4-,0~ℕ[log(1 − 𝑔 8𝑥, 𝑦 )] + & 3 𝔼-, 40~ℕ[log(1 − 𝑔 𝑥, 8𝑦 )] =: ℒ6789 𝑔(𝑥, 𝑦) = 𝑠𝑖𝑔𝑚𝑜𝑖𝑑( ̅𝑥: 𝑊 B𝑦) Mutual Information Maximization 18 Following Yeh and Chen (2019), we maximize MI of QA pairs as follows: [Yeh 2019] Yeh and Chen, QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization EMNLP 2019 Negative Examples
- 19. Training Info-HCVAE 19 We maximizethe following objective with Monte Carlo estimators 𝑚𝑎𝑥!ℒ"#$%& + 𝜆ℒ'()* Θ ≔ {𝜙, 𝜓, 𝜃, 𝑊}
- 20. Experimental Setup 20 1) Tasksand Evaluation Metric • QA generation: QA-based Evaluation, Reverse QA-based Evaluation • Semi-supervised QA: F1 and Exact Match (EM) 2) Data • HarvestingQA (Du and Cardie, 2018) • SQuAD • Natural Questions • TriviaQA
- 21. Experimental Setup 21 3) Baselines •Harvesting-QG [Du 2018]: Seq2Seq + attention + copy • Maxout-QG [Zhao 2018]: Seq2Seq + attention + Maxout copy + BERT encoder • Semantic-QG [Zhang 2019]: Seq2Seq + attention + Maxout copy + BERT encoder + Reinforcement [Du 2018] Du and Cardie, Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia , NAACL2018 [Zhao2018] Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks, EMNLP 2018 [Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019
- 22. QA-based Evaluation (QAE) 22 TrainQA model with synthetic data and measure F1, EM with annotated data. Synthetic data Annotated data Train Test [Zhang 2019] Zhang and Bansal, Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering, EMNLP2019
- 23. Reverse QA-based Evaluation(R-QAE) 23 Accuracy (F1, EM) of the QA model trained with annotated data, evaluated on synthetic QA pairs. Train Test Annotated data Synthetic Data
- 24. QA Generation onSQuAD 24 Method QAE(↑) R-QAE(↓) SQuAD (EM / F1) Harvesting-QG Maxout-QG Semantic-QG HCVAE Info-HCVAE 55.11 / 66.40 56.08 / 67.50 60.49 / 71.91 69.46 / 80.79 71.18 / 81.51 66.77 / 77.85 62.49 / 78.24 74.23 / 88.54 37.57 / 61.24 38.80 / 60.73
- 25. QA Generation onSQuAD 25 More data efficient compared to the other baselines 61.38
- 26. QA Generation onNatural Questions 26 Method QAE(↑) R-QAE(↓) Natural Questions (EM / F1) Harvesting-QG Maxout-QG Semantic-QG HCVAE Info-HCVAE 27.91 / 41.23 30.98 / 44.96 30.59 / 45.29 31.45 / 46.77 37.18 / 51.46 49.89 / 70.01 49.96 / 70.03 58.42 / 79.23 32.78 / 55.12 29.39 / 53.04
- 27. QA Generation onTriviaQA 27 Method QAE(↑) R-QAE(↓) TriviaQA (EM / F1) Harvesting-QG Maxout-QG Semantic-QG HCVAE Info-HCVAE 21.32 / 30.21 24.58 / 34.32 27.54 / 38.25 30.20 / 40.88 35.45 / 44.11 29.75 / 47.73 31.56 / 49.92 37.45 / 58.15 34.41 / 48.16 21.65 / 37.65
- 28. QA Generation Examples(One-to-Many) 28 Input (paragraph and answer) The Scotland act 1998 which was passed by and given royal assent by queen Elizabeth ii on 19 November 1998, governs functions and role of the Scottish parliament and delimits its legislative competence . . . Q1: which act was passed in 1998? Q2: which act governs role of the Scottish parliament? Q3: which act was passed by queen Elizabeth ii? Q4: which act gave the Scottish parliament the responsibility to determine its legislative policy? We sample the question latent variables multiple times with a fixed answer. 𝑧! ~ 𝑝"(𝑧!|𝑐)
- 29. QA Generation Examples(Latent Interpolation) 29 We generate QA pairs by interpolating between two latent codes. Input (paragraph and answer) Atop the main building’ s gold dome is a golden statue of the virgin mary. ... Next to the main building is the basilica of the sacred heart. Immediately behind the basilica is the grotto, ...a marian place of prayer and reflection. ... At the end of the main drive ..., is a simple, modern stone statue of mary. Interpolation
- 30. QA Generation Examples(Latent Interpolation) 30 Q1: where is the grotto at? A1: A marian place of prayer and reflection We generate QA pairs by interpolating between two latent codes. Input (paragraph and answer) Atop the main building’ s gold dome is a golden statue of the virgin mary. ... Next to the main building is the basilica of the sacred heart. Immediately behind the basilica is the grotto, ...a marian place of prayer and reflection. ... At the end of the main drive ..., is a simple, modern stone statue of mary. Interpolation
- 31. QA Generation Examples(Latent Interpolation) 31 Q1: where is the grotto at? A1: A marian place of prayer and reflection Q2: what place is behind the basilica of prayer? A2: grotto We generate QA pairs by interpolating between two latent codes. Input (paragraph and answer) Interpolation Atop the main building’ s gold dome is a golden statue of the virgin mary. ... Next to the main building is the basilica of the sacred heart. Immediately behind the basilica is the grotto, ...a marian place of prayer and reflection. ... At the end of the main drive ..., is a simple, modern stone statue of mary.
- 32. QA Generation Examples(Latent Interpolation) 32 Q1: where is the grotto at? A1: A marian place of prayer and reflection Q2: what place is behind the basilica of prayer? A2: grotto Q3: what is next to the main building at notre dame? A3: the basilica of the sacred heart We generate QA pairs by interpolating between two latent codes. Input (paragraph and answer) Interpolation Atop the main building’ s gold dome is a golden statue of the virgin mary. ... Next to the main building is the basilica of the sacred heart. Immediately behind the basilica is the grotto, ...a marian place of prayer and reflection. ... At the end of the main drive ..., is a simple, modern stone statue of mary.
- 33. QA Generation Examples(Latent Interpolation) 33 Q1: where is the grotto at? A1: A marian place of prayer and reflection Q2: what place is behind the basilica of prayer? A2: grotto Q3: what is next to the main building at notre dame? A3: the basilica of the sacred heart Q4: what is at the end of the main drive? A4: stone statue of mary We generate QA pairs by interpolating between two latent codes. Input (paragraph and answer) Interpolation Atop the main building’ s gold dome is a golden statue of the virgin mary. ... Next to the main building is the basilica of the sacred heart. Immediately behind the basilica is the grotto, ...a marian place of prayer and reflection. ... At the end of the main drive ..., is a simple, modern stone statue of mary.
- 34. Semi-supervised QA 34 Sample 10different QA pairs for a single paragraph from target datasets (+S×10, +N×10, +T×10) Info-HCVAE Input (paragraph) Philadelphia has more murals than any other u.s. city, thanks in part to the 1984 creation of the department of recreation’s mural arts program, . . . The program has funded more than 2,800 murals Q1: which city has more murals than any other city? A1: Philadelphia Q2: why Philadelphia has more murals? A2: the 1984 creation of the department of recreation’s mural arts program . . . Q9: when did the department of recreation’s mural arts program start? A9: 1984 Q10: how many murals funded the graffiti arts program by the department of recreation? A10: more than 2,800
- 35. Semi-supervised QA 35 Generate aQA pair for each paragraph from HarvestingQA dataset (+H×10% ~ +H×100%) Info-HCVAE Input (paragraph) … The typical division is into three branches: a legislature, an executive, and a judiciary, which is the trias politica model Q: what are the three branches of the government ? A: a legislature, an executive, and a judiciary,
- 36. Semi-supervised QA onSQuAD 36 Data EM F1 SQuAD 80.25 88.23 +𝐒×𝟏𝟎 81.20 (+0.95) 88.36 (+0.13) +𝐇×𝟏𝟎𝟎% 81.03 (+0.78) 88.79 (+0.56) +𝐒×𝟏𝟎 + 𝐇×100% 81.44 (+1.19) 88.72 (+0.49) Info-HCVAE +𝐒×𝟏𝟎 82.09 (+1.84) 89.11 (+0.88) +𝐇×𝟏𝟎𝟎% 82.37 (+2.12) 89.63 (+1.40) +𝐒×𝟏𝟎 + 𝐇×𝟏𝟎𝟎% 82.19 (+1.94) 89.84 (+1.59) Semantic-QG (baseline)
- 37. Semi-supervised QA onNatural Questions 37 Data EM F1 SQuAD 42.77 57.29 +𝐍×𝟏 +𝐍×𝟐 +𝐍×𝟑 +𝐍×𝟓 +𝐍×𝟏𝟎 Natural Questions 46.70 (+3.94) 46.95 (+4.19) 47.73 (+4.96) 48.19 (+5.42) 48.44 (+5.67) 61.55 61.08 (+3.79) 61.34 (+4.05) 61.98 (+4.69) 62.21 (+4.92) 62.69 (+5.40) 73.91
- 38. Semi-supervised QA onTriviaQA 38 Data EM F1 SQuAD 48.96 57.98 +𝐓×𝟏 +𝐓×𝟐 +𝐓×𝟑 +𝐓×𝟓 +𝐓×𝟏𝟎 TriviaQA 49.65 (+0.69) 50.01 (+1.05) 49.71 (+0.75) 50.14 (+1.18) 49.65 (+0.69) 64.55 59.13 (+1.21) 59.08 (+1.10) 59.49 (+1.51) 59.21 (+1.23) 59.20 (+1.22) 70.42
- 39. Conclusion 39 • We proposea novel probabilistic generative model for one-to-many QA generation • By maximizing mutual information of QA pairs, we improve the semantic consistency of QA pairs. • Results show that we significantly improve the performance of BERT QA model by further training it with our generated QA pairs. • Gap between semi-supervised and supervised learning, due to the discrepancy among different domains: we hope future research can close the gap. Codes available at https://github.com/seanie12/Info-HCVAE
- 40.