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PhD Final Exam – Mingbo Ma

Structured Neural Models for Natural Language Processing

Most tasks in natural language processing (NLP) involves structured information from both inputs (e.g., a sentence or a paragraph) and output (e.g., a tag sequence, a parse tree or a translated sentence). While neural models achieve great successes in other domains such as computer vision, applying those frameworks to NLP remains challenging for the following reasons. On the source side, we are dealing with input sentences with very complex structures. A simple local swap between two adjacent words could lead to opposite meanings. In addition, input sentences are often noisy as they are collected from real-world scenarios, e.g. online reviews or tweets. Our models need to be able to deal with syntactic variety and polysemy. On the target side, we are often expected to generate structured outputs like translated sentences or parse trees, by searching over an exponentially large search space. In this case, when the exact search is intractable, we resort to inexact search methods such as beam search. In this thesis, we start by introducing several classification algorithms with structured information from the source side but unstructured outputs (sentence level classifications, e.g., sentiment analysis). Then we explore models which generate structured output from the unstructured input signal (e.g., image captioning). Finally, we investigate more complex frameworks that deal with structured information on both input and output sides (e.g., machine translation).

Major Advisor: Liang Huang
Committee: Alan Fern
Committee: Xiaoli Fern
Committee: Prasad Tadepalli
GCR: Kyle Niemeyer

Wednesday, September 5, 2018 at 10:00am to 12:00pm

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


College of Engineering, Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

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