Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer. By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used. We also propose a new module termed keyword-net, which helps the model better capture the key information in the target answer and generate an appropriate question. Experimental results demonstrate that our answer separation method significantly reduces the number of improper questions which include answers. Consequently, our model significantly outperforms previous state-of-the-art NQG models.

1. Improving Neural Question Generation Using Answer
Separation
Seoul National Univ.
Machine Intelligence Lab.
Yanghoon Kim
1

2. Authors
2
Yanghoon Kim
Seoul National Univ.
Hwanhee Lee
Seoul National Univ.
Kyomin Jung
Seoul National Univ.
Joongbo Shin
Seoul National Univ.

3. Contents
3
Background – related work, task definition
Problem – what to solve?
Contribution
Model
Experiments – verifying the effectiveness of the model
Conclusion
Discussion

4. Background
• Neural question generation (NQG)
• Generating a question from a given passage with deep neural
networks
• Importance of NQG
• Generating questions for educational materials.
• Generating questions for improving QA systems.
4

5. Background
5
• Sequence-to-sequence
(seq2seq) • Generate a sequence from another
sequence
• Encoder: input sequence
reader
• Decoder: new sequence
generator
• Structure
• RNN / CNN / Attention
• Pass the information from encoder
to decoder
• Initialize decoder state with
last hidden state of the
encoder

6. Background
6
• Seq2seq with attention
mechanism
• Attention
• Every decoding step:
Attention to every encoder
hidden state
• Determine which part should
be more attention

7. Background
• Task Definition
• Given a passage 𝑋 𝑝
= 𝑥1
𝑝
, … , 𝑥 𝑛
𝑝
and a target answer 𝑋 𝑎
=
𝑥1
𝑎
, … , 𝑥 𝑚
𝑎 as input, the NQG model aims to generate a question
𝑌 = 𝑦1, … , 𝑦 𝑇 asking about the target answer in the passage.
7
ത𝑌 = 𝑎𝑟𝑔𝑚𝑎𝑥
𝑌
𝑃(𝑌|𝑋 𝑝
, 𝑋 𝑎
)
= 𝑎𝑟𝑔𝑚𝑎𝑥
𝑌
෍
𝑡=1
𝑇
𝑃(𝑦𝑡|𝑋 𝑝, 𝑋 𝑎, 𝑦<𝑡)

8. Background
• <Learning to Ask: Neural Question Generation for Reading
Comprehension> (Du et al., ACL 2017)
• The first NN-based question generation
• Simple seq2seq with attention mechanism
• Sentence-level model and Paragraph-level model
• Difference in initialization of the decoder’s hidden states
8
Decoder
Sentence-level
Encoder
Paragraph-level
Encoder
The last
encoder
state
Decoder
Sentence-level
Encoder
Concatenation
of Last states
Sentence-level NQG Paragraph-level NQG

9. Background
• <Neural question generation from text: A preliminary study>
(Zhou et al., NLPCC 2017)
• The first NQG with given target answer
• Feature-rich encoder
• Concatenation of word vector, answer position feature(BIO tagging)
and lexical features(word case, POS, NER)
• Copy Mechanism*
• Pointing mechanism to copy words from encoder’s input sentence
9
* Gulcehre et al., Pointing the unknown words, ACL 2016

10. Background
• Others
• Jointly learning of QA and QG
• <Question Answering and Question Generation as Dual Tasks> (AAAI
2018)
• <Learning to Collaborate for Question Answering and Asking> (NAACL
2018)
• NQG without target answer
• <Identifying Where to Focus in Reading Comprehension for Neural
Question Generation) (EMNLP 2017)
• <A Multi-Agent Communication Framework for Question-Worthy
Phrase Extraction and Question Generation> (AAAI 2019)
• Paragraph-level NQG
• <Paragraph-level Neural Question Generation with Maxout Pointer
and Gated Self-attention Networks> (EMNLP 2018)
10

11. Problem
• Previous NQG systems suffer from a critical problem
• Some models don’t take the question target into account.
• RNNs often follow a shallow generation process.
11
𝑃𝑎𝑠𝑠𝑎𝑔𝑒
𝑃𝑎𝑠𝑠𝑎𝑔𝑒
𝐴𝑛𝑠𝑤𝑒𝑟1
𝐴𝑛𝑠𝑤𝑒𝑟2
𝑄𝑢𝑒𝑠𝑡𝑖𝑜𝑛1
𝑄𝑢𝑒𝑠𝑡𝑖𝑜𝑛2
Model
Ignored
Ignored

12. Problem
• Previous NQG systems suffer from a critical problem
• Some models can’t well grasp the target answer(question target) .
• A sophisticated proportion of generated questions include word in the
target answer.
• Contribution of Copy mechanism
12

13. Contribution
• We propose answer-separated seq2seq
• Treats the target answer(question target) and the passage
separately.
• Prevent the generated question to include words in the target answer.
• Better capture the information from both the target answer and the
passage
• Keyword-net
• Model is consistently aware of the target answer.
• Extract the key information in the target answer.
13

14. Model
• Base model
• Encoder-decoder with attention
• Answer-separated seq2seq
• Answer-separated passage encoder
• Answer encoder
• Answer-separated decoder
• keyword-net
• Word generator with semantic information
14

15. Model
• Answer-separated passage encoder
• A simple preprocessing of the input passage
• Original passage: John Francis O’Hara was elected president of Notre
Dame in 1934
• Masked passage: <answer> was elected president of Notre Dame in
1934
• A one-layer bi-LSTM
• Answer encoder
• A one-layer bi-LSTM
15

16. Model
• Answer-separated decoder
• A one-layer LSTM
• Functionality: Combine features from encoders and generate
sequence output
• The model still can generate questions without target answer
information
• What if the target answer is long and complicated?
16
Passage: Steve Jobs is the founder of Apple
Processed passage: <answer> is the founder of Apple
Generated question: Who is the founder of Apple?

17. Model
• Answer-separated decoder
• A one-layer LSTM
• keyword-net
• Consistently be aware of the target answer.
• Extract key information.
• Passage: Steve Jobs is the founder of Apple
• Answer: founder of Apple
17

18. Model
• Answer-separated decoder
• Capturing semantic information
• retrieval style word generation layer proposed by (Ma et al. 2018)*
• seq2seq has tendency to memorize the sequence pattern rather than
reflecting word meanings
18* Ma et al., Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase
Generation, NAACL 2018

19. Model
• Trick 1: Named entity replacement
• Purpose: Improve model performance
• Motivation: Most named entities do not appear frequently
19
Steve Paul Jobs was the chairman, chief executive officer (CEO), and co-
founder of Apple; chairman and majority shareholder of Pixar.
Original passage
𝑃𝑒𝑟𝑠𝑜𝑛1 was the chairman, chief
executive officer (CEO), and co-founder
of 𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛1; chairman and
majority shareholder of 𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛2
Original word NER tag
Steve Paul Jobs 𝑃𝑒𝑟𝑠𝑜𝑛1
Apple 𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛1
Pixar 𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛2
Processed passage Matching table

20. Model
• Trick 2: Removal of duplicates(post-processing)
• Purpose: Improve model performance
• Motivation: Recurrent model tends to repeat the same pattern if
not trained well
20
Who is the the CEO of the CEO of Apple?
Generated question
Who is the CEO of the CEO of Apple?
1-gram removal
Who is the CEO of Apple?
3-gram removal

21. Experiment
• Data
• Processed version of SQuAD 1.1
• Data split 1: 70,484/10,570/11,877 (train/dev/test)
• Data split 2: 86,635/8,965/8,964
• Evaluation
21
* Song et al., Leveraging Context Information for Natural Question Generation, NAACL 2018
*

22. Experiment
• Impact of answer separation
• Capturing target answer
• Check if the target answer is included in the generated question
• AP : Answer position Feature (BIO scheme)
• (Song et al. 2018) used the copy mechanism.
22

23. Experiment
• Impact of answer separation
• Interrogative word prediction
• “What” takes up more than half of the whole training set
• “Which” : “Which year” can be represented as “When”
• “why”, “yes/no” : only takes up 1.5% and 1.2% of the training set.
23

24. Experiment
• Impact of answer separation
• Attention from <answer>
• (a) is the attention matrix from our model
• (b) is the attention matrix from seq2seq + AP
• <answer> give the highest attention weights to the interrogative word
“who” in (a)
24

25. Experiment
• Question generation for machine comprehension
• Use named entities as target answers, generate synthetic data for
machine comprehension system(QA net by Google).
• ALL : Evaluation result of SQuAD dev set(10k)
• NER : Evaluation result of partial SQuAD dev set(4k)
• answers of single named entity
25

26. Conclusion
• We proposed ASs2s which treats the passage and the target
answer separately to better utilize the information from the
both sides
• The Keyword-net supplements the lack of information
caused by <answer> token.
• We achieved state-of-the-art performances in processed
SQuAD 1.1. We also verified that our answer separation
technique has actual help in preventing answer copying
26

27. Discussion
• Qualitative analysis on Keyword-net
• Currently we have only checked the performance gain from the
Keyword-net
• Data Augmentation for Machine Reading task
• How to select new answers that is not in the original dataset
27

28. 28
Thank you!
<Yanghoon Kim>
https://yanghoonkim.github.io/20181105/me