Question Answeringspeech.ee.ntu.edu.tw/~tlkagk/courses/DLHLP20/QA (v12).pdf · 2020. 6. 28. · •SQuAD [Rajpurkar, et al., EMNLP’ܙܞ], DRCD [Shao, et al., arXiv’ܙܠ] Source

Question AnsweringHung-yi Lee

李宏毅

Knowledge source question

answer

Question Answering

Who is the U.S. president?

Who should pay for the date, and why?

Is Trump older than than Obama?

• word• span in source• correct choice• paragraph

Module for Source Module for QuestionAttention

Module for Answer

Answer is simply a word

• bAbI• MNIST of QA

• 20 types of questions

• Synthesized

Whether a system can answer questions via chaining facts, simple induction, deduction, etc.

[Weston, et al., arXiv’15]

Answer is simply a word


Module for Answer


Classification problem!

John Mary Kitchen No Maybe

Multiple Choices

Module for SourceModule for Question


(Ignoring attention here for simplicity)

Module for Choice

Choice A

Module for Answer

Yes / No

Knowledge source question Choice A Choice B

Choice C Choice D

Span in Source / Extraction-based

• SQuAD [Rajpurkar, et al., EMNLP’16], DRCD [Shao, et al., arXiv’18]

Source w1 w2 w3 w4 w5 w6 w7

StartScore

EndScore

0.1 0.1 0.7 0.1 0.0 0.0 0.0

0.0 0.0 0.0 0.2 0.6 0.2 0.0

The answer is w3 w4 w5

Module for SourceModule for Question

AttentionKnowledge source question

0.7

0.0

0.1

0.2

0.1

0.6

0.1

0.2

0.0

0.0

Module for Answer

LSTM

start end

Before BERT:

After BERT:

• MS MARCO [Bajaj, et al., NIPS’16], DuReader [He, et al., ACL workshop’18]

Free Answer Generation

S-net[Tan, et al., AAAI’18]

並不是所有問題都有答案 ……

Know what you don’t know

• SQuAD 2.0 [Rajpurkar, et al., ACL’18]

Know what you don’t know

Source w1 w2 w3 w4 w5 w6 w7

StartScore

EndScore

0.1 0.1 0.0 0.1 0.0 0.0 0.0

0.0 0.0 0.0 0.2 0.60.2 0.0

Null

0.7

0.0

This is used in the original BERT paper by considering [CLS] as Null.

[Devlin, et al., NAACL’19]


answer


Module for Answer Answerable?

Yes/No[Liu, et al., arXiv’18]

questionKnowledge source answer

Answer Verifier Yes/No

[Hu, et al., AAAI’19]


answer

Question Answering






Module for Answer

Internet

[Chen, et al., ACL‘17]

MS MARCO and DuReader also have several documents for each question.

Not all the documents are relevant

DrQA

V-Net [Wang, et al., ACL’18]

Visual

• source: picture/video

• What is in the image?

• Are there any humans?

• What sport is being played?

• Who has the ball?

• How many players are in the image?

• Who are the teams?

• Is it raining?

source: http://visualqa.org/

A vector for each region

Audio

• TOEFL Listening Comprehension Test by Machine

Question: “ What is a possible origin of Venus’ clouds? ”

Audio Story:

Choices:

(A) gases released as a result of volcanic activity

(B) chemical reactions caused by high surface temperatures

(C) bursts of radio energy from the plane's surface

(D) strong winds that blow dust into the atmosphere

(The original story is 5 min long.)

Link: https://github.com/iamyuanchung/TOEFL-QA

[Tseng, et al., INTERSPEECH’16]

Audio – SQuAD Style

• Link: https://github.com/chiahsuan156/ODSQA

SPOKEN OPEN-DOMAIN QUESTION ANSWERING DATASET

https://github.com/chiahsuan156/ODSQA

Audio – SQuAD Style

• Link: https://github.com/chiahsuan156/ODSQA

SPOKEN OPEN-DOMAIN QUESTION ANSWERING DATASET

SOD QA

OPEN-DOMAIN SPOKEN QUESTION ANSWERING DATASET

ODS QA[Lee, et al., SLT’18]

https://github.com/chiahsuan156/ODSQA

Audio - Techniques Developed

Subword UnitsAdversarial Learning

[Lee, et al., INTERSPEECH’18] [Lee, et al., ICASSP’19]

Audio - Towards End-to-end

[Chuang, et al., arXiv’19]

Movie QA

[Tapaswi, et al., CVPR’16]

QACNN

[Liu, et al., ICCV workshop’18]


answer

Question Answering






Module for Answer

Types of Questions

Simple Question: Match & Extract

Complex Question: Reasoning

Dialogue QA

Difficulty

Types of Questions



Dialogue QA

Simple Questions

SQuAD

Match & Extract

Module for Source Module for Question


merge the whole question into one vector

Attention

𝛼1 𝛼2 𝛼3 𝛼4 𝛼5

𝑛=1

5

𝛼𝑛𝑥𝑛

Query-to-context Attention

Module for Answer

Classifier?

Predicting time span?

Match

Extract

𝑥𝑛

Query-to-context Attention

End-to-end Memory Network[Sukhbaatar, et al., NIPS’15]



Attention

𝑛=1

5

𝛼𝑛𝑥𝑛

Query-to-context Attention (v2)

Module for Answer

𝛼11 𝛼2

1 𝛼31 𝛼4

1 𝛼51

𝛼12 𝛼2

2 𝛼32 𝛼4

2 𝛼52

𝛼13 𝛼2

3 𝛼33 𝛼4

3 𝛼53

max

multiple vectors

Query-to-context Attention (v2)

[Xu, et al., CVPR’16]



Context-to-query Attention

Attention

𝛼1 𝛼2 𝛼3

𝑛=1

3

𝛼𝑛𝑥𝑛

Module for Answer

RnetR-Net

Self-attention

[Wang, et al., ACL’17]

[Huang, et al., ICLR’18]

Self-attention

Bi-directional Attention Flow

• BiDAF

Context Context

Qu

ery

Qu

ery

[Seo, et al., ICLR’17]

Dynamic Coattention Networks

[Xiong, et al., ICLR’17]

New query

QANetBERT 家族之外的最後一道榮光

[Yu, et al., ICLR’18]

Do not use RNN

BERT

你想要的 BERT 裡面通通都有了!


start endAnswerModule

Context-to-query Query-to-context

Self-matching, self-boosting

Types of Questions



Dialogue QA

Qangaroo[Welbl, et al., TACL’18]

Hoppot QA [Yang, et al., EMNLP’18]

懷抱惡意出題

DROP Discrete Reasoning Over the text in the Paragraph[Dua, et al., NAACL’19]

Multiple-hop

Match Extract Change

Match Extract

Match Extract

Change

……

(“frog”)

(“Brian”)

(“yellow”)

MemoryNetwork

q

……

……

Match

Extract

……

……

Match

Extract

Change what to match

Change what to match

How many hops do we need?

ReasoNet[Shen, et al., KDD’17]

Graph Neural Network

[Qiu, et al., ACL’19]

Graph neural network (part 1): https://youtu.be/eybCCtNKwzAGraph neural network (part 2): https://youtu.be/M9ht8vsVEw8

Graph Neural Network

[Qiu, et al., ACL’19]

[Shao, et al., arXiv’20]

Graph is not needed …

Types of Questions



Dialogue QA

Dialogue QA

CoQA[Reddy, et al., TACL’19]

Dialogue QA

[Choi, et al., EMNLP’18]

QuAC

Dialogue QA

D Q1

D Q2

D Q3

QA Model

QA Model

QA Model

A1

A2

A3

……

……

Dialogue QA

D Q1

D Q2

D Q3

l-th layer (l+1)-th layer

RNN

RNN

[Huang, et al., ICLR’19]

Dialogue QA

D Q1

D Q2

D Q3

l-th layer (l+1)-th layer

0.1

0.3

0.6

Attention Weight

[Qu, et al., CIKM’19]

Problem Solved?

https://rajpurkar.github.io/SQuAD-explorer/

Train on SQuAD, Test on bAbI

感謝吳侑學同學提供實驗結果

[Kaushik, et al., EMNLP’18]

[Feng, et al., EMNLP’18]

機器居然料事如神!

Human Performance

機器超越人類?

VQA [Agrawal, et al., CVPR’18]

VQA

(question type only)

(question only)

[Agrawal, et al., CVPR’18]

There is still a long way to go.

Reference

• Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin and Tomas Mikolov. Towards AI Complete Question Answering: A Set of Prerequisite Toy Tasks, arXiv, 2015

• Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang, SQuAD: 100,000+ Questions for Machine Comprehension of Text, EMNLP, 2016

• Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang, MS MARCO: A Human Generated MAchine Reading COmprehension Dataset, NIPS, 2016

• Chih Chieh Shao and Trois Liu and Yuting Lai and Yiying Tseng and Sam Tsai, DRCD: a Chinese Machine Reading Comprehension Dataset, arXiv, 2018

• Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, YizhongWang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang, DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications, ACL workshop, 2018

• Chuanqi Tan, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, Ming Zhou, S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension, AAAI, 2018

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Reference

• [Chen, et al., ACL‘17] Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes, Reading Wikipedia to Answer Open-Domain Questions, ACL, 2017

• Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning, HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering, EMNLP, 2018

• Pranav Rajpurkar, Robin Jia, Percy Liang, Know What You Don't Know: Unanswerable Questions for SQuAD, ACL, 2018

• Siva Reddy, Danqi Chen, Christopher D. Manning, CoQA: A Conversational Question Answering Challenge, TACL, 2019

• [Choi, et al., EMNLP’18] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer, QuAC : Question Answering in Context, EMNLP, 2018

• [Qiu, et al., ACL’19] Lin Qiu, Yunxuan Xiao, Yanru Qu, Hao Zhou, Lei Li, WeinanZhang, Yong Yu, Dynamically Fused Graph Network for Multi-hop Reasoning, ACL, 2019

• [Shao, et al., arXiv’20] Nan Shao, Yiming Cui, Ting Liu, Shijin Wang, Guoping Hu, Is Graph Structure Necessary for Multi-hop Reasoning?, arXiv, 2020

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Reference

• Yizhong Wang, Kai Liu, Jing Liu, Wei He, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang, Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification, ACL, 2018

• Bo-Hsiang Tseng, Sheng-syun Shen, Hung-Yi Lee, Lin-Shan Lee, Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine, INTERSPEECH, 2016

• Chia-Hsuan Lee, Szu-Lin Wu, Chi-Liang Liu, Hung-yi Lee, Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension, INTERSPEECH, 2018

• Chia-Hsuan Lee, Yun-Nung Chen, Hung-Yi Lee, "Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation", ICASSP, 2019

• Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang, Hung-Yi Lee, ODSQA: Open-domain Spoken Question Answering Dataset, SLT, 2018

• Yung-Sung Chuang, Chi-Liang Liu, Hung-Yi Lee, Lin-shan Lee, SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering, arXiv, 2019

• Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel

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Reference

• Tzu-Chien Liu, Yu-Hsueh Wu, Hung-Yi Lee, Query-based Attention CNN for Text Similarity Map, ICCV workshop, 2018

• Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih, FlowQA: Grasping Flow in History for Conversational Machine Comprehension, ICLR, 2019

• Xiaodong Liu, Wei Li, Yuwei Fang, Aerin Kim, Kevin Duh, Jianfeng Gao, Stochastic Answer Networks for SQuAD 2.0, arXiv, 2018

• Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL, 2019

• Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, Dongsheng Li, Read + Verify: Machine Reading Comprehension with Unanswerable Questions, AAAI, 2019

• Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner, DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs, NAACL, 2019

• Johannes Welbl, Pontus Stenetorp, Sebastian Riedel, Constructing Datasets for Multi-hop Reading Comprehension Across Documents, TACL, 2018

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Reference

• Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi, Bidirectional Attention Flow for Machine Comprehension, ICLR, 2017

• Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le, QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension, ICLR, 2018

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• Huijuan Xu, Kate Saenko. Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering, CVPR, 2016

• Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, End-To-End Memory Networks, NIPS, 2015

• Caiming Xiong, Victor Zhong, Richard Socher, Dynamic Coattention Networks For Question Answering, ICLR, 2017

• Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, Ming Zhou, Gated Self-Matching Networks for Reading Comprehension and Question Answering, ACL, 2017

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Reference

• Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer, Attentive History Selection for Conversational Question Answering, CIKM, 2019

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• Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen, ReasoNet: Learning to Stop Reading in Machine Comprehension, KDD, 2017
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