Dan Goldwasser,
Roi Reichart,
James Clarke and
Dan Roth.
2011.
Confidence Driven Unsupervised Semantic Parsing.. In
ACL, pp
1486--1495. cit
29. [
semparse, d=geo]
pdf abstract google scholar
Current approaches for semantic parsing take a supervised approach requiring a consider- able amount of training data which is expen- sive and difficult to obtain. This supervision bottleneck is one of the major difficulties in scaling up semantic parsing.
We argue that a semantic parser can be trained effectively without annotated data, and in- troduce an unsupervised learning algorithm. The algorithm takes a self training approach driven by confidence estimation. Evaluated over Geoquery, a standard dataset for this task, our system achieved 66% accuracy, com- pared to 80% of its fully supervised counter- part, demonstrating the promise of unsuper- vised approaches for this task.
Dan Goldwasser and
Dan Roth.
2011.
Learning from natural instructions. In
Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Three, pp
1794--1800.
AAAI Press. cit
15. [
semparse, d=freecell]
pdf abstract google scholar
Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requir- ing a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner us- ing natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learn- ing process.
In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of ma- chine learning is motivated by human learning pro- cesses, in which the learner is given a lesson de- scribing the target concept directly, and a few in- stances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. This approach alleviates the supervision burden of traditional machine learn- ing by focusing on supplying the learner with only human-level task expertise for learning.
We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also general- izes to previously unseen instructions.