Towards an implementable dependency grammar

The aim of this paper is to define a dependency grammar framework which is both linguistically motivated and computationally parsable. See the demo at http://www.conexor.fi/analysers.html#testingComment: 10 page

Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

CCG contextual labels in hierarchical phrase-based SMT

In this paper, we present a method to employ target-side syntactic contextual information in a Hierarchical Phrase-Based system. Our method uses Combinatory Categorial Grammar (CCG) to annotate training data with labels that represent the left and right syntactic context of target-side phrases. These labels are then used to assign labels to nonterminals in hierarchical rules. CCG-based contextual labels help to produce more grammatical translations by forcing phrases which replace nonterminals during translations to comply with the contextual constraints imposed by the labels. We present experiments which examine the performance of CCG contextual labels on Chinese–English and Arabic–English translation in the news and speech expressions domains using different data sizes and CCG-labeling settings. Our experiments show that our CCG contextual labels-based system achieved a 2.42% relative BLEU improvement over a PhraseBased baseline on Arabic–English translation and a 1% relative BLEU improvement over a Hierarchical Phrase-Based system baseline on Chinese–English translation

Expressive Power of Hypergraph Lambek Grammars

Hypergraph Lambek grammars (HL-grammars) is a novel logical approach to generating graph languages based on the hypergraph Lambek calculus. In this paper, we establish a precise relation between HL-grammars and hypergraph grammars based on the double pushout (DPO) approach: we prove that HL-grammars generate the same class of languages as DPO grammars with the linear restriction on lengths of derivations. This can be viewed as a complete description of the expressive power of HL-grammars and also as an analogue of the Pentus theorem, which states that Lambek grammars generate the same class of languages as context-free grammars. As a corollary, we prove that HL-grammars subsume contextual hyperedge replacement grammars

Learning Parse and Translation Decisions From Examples With Rich Context

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.Comment: 8 pages, LaTeX, 3 postscript figures, uses aclap.st

A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System

We present the dialogue component of the speech-to-speech translation system VERBMOBIL. In contrast to conventional dialogue systems it mediates the dialogue while processing maximally 50% of the dialogue in depth. Special requirements like robustness and efficiency lead to a 3-layered hybrid architecture for the dialogue module, using statistics, an automaton and a planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in Proceedings of EACL-95, Dublin