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Introduction Karl Stratos Rutgers University Karl Stratos CS 533: - PowerPoint PPT Presentation

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CS 533: Natural Language Processing Introduction Karl Stratos Rutgers University Karl Stratos CS 533: Natural Language Processing 1/10 Modern Natural Language Processing (NLP) NLP is everywhere Other examples? Karl Stratos CS 533: Natural

  1. CS 533: Natural Language Processing Introduction Karl Stratos Rutgers University Karl Stratos CS 533: Natural Language Processing 1/10

  2. Modern Natural Language Processing (NLP) NLP is everywhere Other examples? Karl Stratos CS 533: Natural Language Processing 2/10

  3. Short-Term Goals Make machines understand human language Countless applications: machine translation (MT), personal assistant, crucial component in any AI system (e.g., autonomous driving) Karl Stratos CS 533: Natural Language Processing 3/10

  4. Long-Term Goals Make machines as intelligent and conscious as humans (or more) The Turing test Her (2013) Karl Stratos CS 533: Natural Language Processing 4/10

  5. Some History ◮ 1950: Alan Turing proposes the Turing test ◮ 1954: Georgetown–IBM experiment (rule-based MT) “Within three or five years, machine translation will be a solved problem” ◮ 50-90s: focus on rule-based AI systems (e.g., SHRDLU) ◮ From early 90s: Rise of statistical/data-driven NLP ◮ IBM: statistical MT and speech recognition “Every time I fire a linguist, the performance of the speech recognizer goes up” -Fred Jelinek ◮ UPenn/AT&T: statistical techniques for tagging and parsing ◮ 2011: IBM Watson wins Jeopardy! against human champions ◮ From early 2010s: Rise of deep learning for NLP ◮ “Human-level” MT: The Great A.I. Awakening ( NYT , 2016) ◮ “Human-level” conversation”: Google Duplex (2018) Karl Stratos CS 533: Natural Language Processing 5/10

  6. Reality “Hey Siri, tell my wife I love her” Karl Stratos CS 533: Natural Language Processing 6/10

  7. Why NLP is Hard: Ambiguity Actual headline in Guardian (1982) “British Left Waffles on Falklands” ◮ Syntactic ambiguity British Left Waffles on Falklands British Left Waffles on Falklands ◮ Lexical ambiguity : Every single word ◮ Semantic ambiguity Karl Stratos CS 533: Natural Language Processing 7/10

  8. Why NLP is Hard: Nonsmoothness A single word can completely change the meaning Karl Stratos CS 533: Natural Language Processing 8/10

  9. Why NLP is Hard: World Knolwedge Winograd (1972) ◮ The city councilmen refused the demonstrators a permit because they feared violence. ◮ The city councilmen refused the demonstrators a permit because they advocated violence. Karl Stratos CS 533: Natural Language Processing 9/10

  10. Course Topics ◮ Language modeling n -gram models, log-linear models, neural language models (feedforward, recurrent) ◮ Conditional language modeling Attention-based models, translation, summarization ◮ Text classification Naive Bayes classifier ◮ Structured prediction Hidden Markov models, probabilistic context-free grammars ◮ Unsupervised learning Expectation maximization algorithm, variational autoencoders ◮ Special topics on various applications Information extraction, question answering, dialogue, grounding (Subject to change) Karl Stratos CS 533: Natural Language Processing 10/10

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