In many areas of NLP reuse of utility tools such as parsers and POS taggers is now common, but this is still rare in NLG. The subfield of surface realisation has perhaps come closest, but at present we still lack a basis on which different surface realisers could be compared, chiefly because of the wide variety of different input representations used by different realisers. This paper outlines an idea for a shared task in surface realisation, where inputs are provided in a common-ground representation formalism which participants map to the types of input required by their system. These inputs are derived from existing annotated corpora developed for language analysis (parsing etc.). Outputs (realisations) are evaluated by automatic comparison against the human-authored text in the corpora as well as by human assessors. 1

This paper presents a method for extracting both syntactic and semantic tags. An extended CFG parser works in conjunction with an HMM model, which handles unknown words and partially known words, to yield a complete syntactic and semantic interpretation of the utterance. Four experiments and applications were performed using the paradigm to show the usefulness of the approach in processing spoken sentences.

This paper presents an approach for learning to translate simple narratives, i.e. texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries. 1

Program Synthesis, which is the task of discovering programs that realize user intent, can be useful in several scenarios: enabling people with no programming background to develop utility programs, helping regular programmers automatically discover tricky/mundane details, program understanding, discovery of new algorithms, and even teaching. This paper describes three key dimensions in program synthesis: expression of user intent, space of programs over which to search, and the search technique. These concepts are illustrated by brief description of various program synthesis projects that target synthesis of a wide variety of programs such as standard undergraduate textbook algorithms (e.g., sorting, dynamic programming), program inverses (e.g., decoders, deserializers), bitvector manipulation routines, deobfuscated programs, graph algorithms, text-manipulating routines, mutual exclusion algorithms, etc. Categories and Subject Descriptors D.1.2 [Programming Techniques]:

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requiring 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 using 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 learning 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 machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly, and a few instances 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 learning 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 generalizes to previously unseen instructions. 1

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... focuses on modeling stochastic dynamic domains, using representations and algorithms that combine logical AI ideas and probabilistic methods. We introduce new tractable and highly accurate algorithms for reasoning in those complex domains. Furthermore, we apply these algorithms to tasks of narrative understanding and web page monitoring. We model stochastic dynamic domains with a factored logical representation that uses a graphical model to represent a prior distribution over initial states. Our representation uses sequences of actions (represented in logical form) to represent transitions. We introduce an algorithm for reasoning in stochastic dynamic domains (in propositional and relational fashions) based on subroutines for reasoning in deterministic substructure of the domain. Our algorithm takes advantage of the factored logical representation and efficient subroutines for logical progression and regression. The tractability of the algorithm results from the tractability of these underlying subroutines. Our theoretical and empirical results show significant improvement of our algorithm over previous approaches for reasoning. Our novel algorithm for reasoning in probabilistic dynamic domains samples sequences of deterministic actions corresponding to an observed sequence of probabilistic actions. This algorithm is built upon a novel exact and tractable