2018 Women in Analytics Conference https://www.womeninanalytics.org/ The goal of this panel is to discuss where individual industries or areas of focus has been successful and unique in utilizing AI as a technology, if/where it is struggling with the adoption and why, and what complexities exist around fully utilizing AI as a solution.

1. How AI understands language
Wei Xu
Department of Computer Science and Engineering
March-15-2018 @ Women in Analytics Conference

2. The Turing Test
Alan Turing (1912-1954) mathematician, world-class Marathon runner, forced to chemical castration for homosexuality conviction.
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3. Natural language processing (NLP)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
865
770 749
660
860 863 930 894 880 823 871 845
732
887 931 887
1,190
1,104
1,620 1,597
1,315
1,583 1,641 1,562
2,123 2,104
1,602
2,151 2,136
2,028 2,064 2,093
1,990
2,536
2,901 2,949
Number of Members
Association for Computational Linguistics is an organization of world leading Natural Language Processing researchers since 1962.
The goal is to enable computers to understand and generate human language.
USA
Worldwide

4. The Holy Grail of AI / NLP
Human language is ambiguous, creative, infinite, and ever evolving.

5. The Holy Grail of AI / NLP
Human language is ambiguous, creative, infinite, and ever evolving.
. . . .
Photo Credit: Kristy Wigglesworth / AP

6. The Holy Grail of AI / NLP
word “selfie” in Google Trends
Human language is ambiguous, creative, infinite, and ever evolving.
Images from http://www.ufunk.net/en/gadgets/selfie-arm/

7. The Holy Grail of AI / NLP
. word
, phrase
. . sentence. .
Solution: learning large-scale paraphrases
Human language is ambiguous, creative, infinite, and ever evolving.
Wei Xu. “Data-driven Approaches for Paraphrasing Across Language Variations” PhD Thesis (2014)

8. Natural language understanding
match
. . , . ,
Question Answering
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. , .
. , . .
.

9. Natural language generation
Text Simplification
(adapting machine translation techniques)
Wei Xu, Courtney Napoles, Ellie Pavlick, Chris Callison-Burch. “Optimizing Statistical Machine Translation for Simplification” in TACL (2016)

10. Designing Various [Machine learning] models
• Multi-instance Learning Models [Xu et al. 2014; Tabassum et al. 2016]
Wei Xu, Alan Ritter, Chris Callison-Burch, Bill Dolan, Yangfeng Ji “Extracting Lexically Divergent Paraphrases from Twitter” in TACL (2014)
(Viterbi approximation + online learning)

11. Designing Various [Machine learning] models
Wuwei Lan, Wei Xu. “The Important Role of Subword Embeddings in Sentence Pair Modeling” in NAACL (2018)
• Deep Pairwise Neural Networks [He et al. 2015; Lan et al. 2017; Lan and Xu 2018]
Bi-LSTM
Decompose sentence input into word context to reduce modeling difficulty
GloVe
or subword CNN
Highway Network

12. Multiple vector similarity measurements used to capture word pair relationship
Designing Various [Machine learning] models
• Deep Pairwise Neural Networks [He et al. 2015; Lan et al. 2017; Lan and Xu 2018]
Wuwei Lan, Siyu Qiu, Hua He, Wei Xu “A Continuously Growing Dataset of Sentential Paraphrases” in EMNLP (2017)

13. Designing Various [Machine learning] models
Hua He and Jimmy Lin. “Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement” in NAACL (2016)
• Deep Pairwise Neural Networks [He et al. 2015; Lan et al. 2017; Lan and Xu 2018]
More attention added to top ranked word pairs.

14. Designing Various [Machine learning] models
• Deep Pairwise Neural Networks [He et al. 2015; Lan et al. 2017; Lan and Xu 2018]
Sentence pair relationship identified by pattern recognition through ConvNet.
Hua He and Jimmy Lin. “Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement” in NAACL (2016)

15. [data science] for linguistic styles
she says he says
wonderfully delightfully beautifully fine well good nicely superbly
Daniel Preotiuc, Wei Xu, Lyle Ungar. “Discovering User Attribute Stylistic Differences via Paraphrasing” in AAAI (2016)
(also studied age & income)

16. Machine reading of instructions
Chaitanya Kulkarni, Wei Xu, Alan Ritter, Raghu Machiraju. “An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols” in NAACL (2018)
Wet Lab Protocols:
(Photo: Moley Robotic Chef)

17. http://web.cse.ohio-state.edu/~weixu/
Thank you
Assistant ProfessorWei Xu
Department of Computer Science and Engineering
Ohio State University