Machine Translation and Semantic Parsing

This Tuesday, we will cover machine translation. Please read chapter 2 of the textbook from Eugene's class:

• Eugene Charniak and Mark Johnson.  Introduction to Computational Linguistics. Chapter 2: Machine Translation.

Thursday's class will be a guest lecture from Yoav Artsi.   There are two readings:

• Yoav Artzi and Luke Zettlemoyer.  Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions.  Transactions of the Association for Computational Linguistics (TACL), 2013.

If you find that one too dense, then this paper might unpack things: 

• Luke Zettlemoyer and Michael Collins. Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars.
  In Proceedings of the Twenty First Conference on Uncertainty in Artificial Intelligence (UAI), 2005.

Dialog

Our previous readings have largely focused on one-off or one-sided interactions.  This week we will think about dialogue, where the agent and human interact continuously using language.  Our first reading will be a philosophical paper by Paul Grice.  The reading for Tuesday's class will be a review paper covering the POMDP approach to dialogue systems, along with a philosophical paper that has influenced a lot of the thinking about dialogue.

• Young, S., Gašić, M., Thomson, B., & Williams, J. D. (2013). POMDP-based statistical spoken dialog systems: A review. Proceedings of IEEE.  101:5.  pages 1160-1179. 
• Grice, H. Paul. Logic and conversation.(1975): 41-58.

For Thursday's class, we will read Adam Vogel's recent work about the emergence of Gricean maxims from DEC-POMDP dialogue models:

• Adam Vogel, Max Bodoia, Christopher Potts, and Dan Jurafsky. Emergence of Gricean Maxims from Multi-Agent Decision Theory. NAACL 2013
• Adam Vogel, Christopher Potts, and Dan Jurafsky. Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs.

Please post on the blog a comment of about 250 words pointing out one advantage and one disadvantage of the POMDP approach to dialogue.  Post this response by 5pm on Sunday evening.  Then by 7am Tuesday morning, post a response to someone else's comment on the blog.

Pick one of the other approaches for understanding commands (Chen & Mooney, Winograd, Matuszek et al., or Branavan et al.,) and describe how it could be integrated with SLAM.    What are the strengths and weaknesses of these representations for understanding language, compared to the approach described in this week's paper?

Semantic Mapping

On Tuesday we will study SLAM, without worrying about language.  SLAM (Simultaneous Localization and Mapping) is an important problem in robotics. 

Tuesday 10/14:

• Durrant-Whyte, Hugh, and Tim Bailey. Simultaneous localization and mapping: part I. Robotics & Automation Magazine, IEEE 13.2 (2006): 99-110.

Then, on Thursday 10/17 we will study one attempt to integrate slam with language:

• Ananth Ranganathan and Frank Dellaert.  Online Probabilistic Topological Mapping.  IJRR 2011.
• Matthew R. Walter, S. Hemachandra, B. Homberg, S. Tellex, and S. Teller. Learning semantic maps from natural language descriptions. In Proceedings of Robotics: Science and Systems (RSS), Berlin, Germany, June 2013 (accepted, to appear). 

Since you are working on your project proposals there is no required blog post assignment.  But feel free to post a question or comment if you'd like!

Project Proposal Presentations

Here is the schedule for project proposal presentations.   Each team will have 15 minutes.  You should plan on talking for five minutes, so that most of the time will be used for discussion and feedback.

Let me know as soon as possible if you are taking the class but don't appear on the schedule.

Tuesday 10/8:

• Lauren Bilsky.  Machine Translation using Grounded Language and Topic Modeling.
• Stephen Brawner.  Task-based User Modeling in Shared Autonomy.
• Do Kook Choe.  Navigation via Machine Translation.
• Jun Ki Lee, Zhiqiang Sui.  Learning Natural Language Commands for Robots in Home Environment Situations.
• Andrew Kovacs and Sam Birch.  Webtalk.

Thursday 10/10:

• Izaak Baker and Nakul Gopalan.  Athena.
• Xiaolu Li , Zhe Zhao.  Automatic Turtle Graphics.
• Miles Eldon and Kurt Spindler.  Comparing Inference Algorithms for Grounding Trajectories.
• Yujie Wan, Lixing Lian.  Learning Semantic Parser from Question­-Answer Pairs.

Tuesday 10/1:

• David Abel and Gabrial Barth-Maron
•  Tom Sgouros. SHRDLU updated: parsing with ambiguity and without rules
• Charles Yeh and Bowei Wang.  Application of SHRDLU in Minecraft. 

Probabilistic Grounding Models (III)

Probabilistic Grounding Models (III)

On Thursday, October 3rd, we will turn back to probabilistic grounding models, looking at one that takes a reinforcement learning approach.

• S. R. K Branavan, Harr Chen, Luke S Zettlemoyer, and Regina Barzilay. Reinforcement learning for mapping instructions to actions. In Proceedings of the Association for Computational Linguistics, page 82–90, 2009. 

This paper won Best Paper at ACL 2009.  Post a response on the blog of about 500 words comparing this paper to the three previous papers in this area.  (Chen & Mooney; Tellex et al., and Matuszek et al.)  How are word meanings represented?  How are they learned?   What training data is required? What else is given to the system as input?