Лагранж Релаксациясы

This paper describes an algorithm for exact decoding of phrase-based translation models, based on Lagrangian relaxation. The method recovers exact solutions, with certificates of optimality, on over 99% of test …...

We describe an exact decoding algorithm for syntax-based statistical translation. The ap- proach uses Lagrangian relaxation to decom- pose the decoding problem into tractable sub- problems, thereby avoiding exhaustive dy- …...

AbstractSet covering algorithms are efficient tools for solving an optimal linguistic corpus reduction. The optimality of such a process is directly related to the descriptive features of the sentences of …...

The search space of Phrase-Based Statisti- cal Machine Translation (PBSMT) systems can be represented under the form of a di- rected acyclic graph (lattice). The quality of this search space …...

Sentence compression is important in a wide range of applications in natural language process- ing. Previous approaches of Japanese sentence compression can be divided into two groups. Word-based methods extract …...

Extractive summarization typically uses sen- tences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, re- sulting in summaries containing more …...

Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We de- velop a new decoding algorithm that combines the …...

Meaning Representation Jeffrey Flanigan Sam Thomson Jaime Carbonell Chris Dyer Noah A. Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {jflanigan,sthomson,jgc,cdyer,nasmith}@cs.cmu.edu Abstract Abstract Meaning Representation (AMR) is …...

We present a novel dependency pars- ing method which enforces two structural properties on dependency trees: bounded block degree and well-nestedness. These properties are useful to better represent the set …...

AbstractWe present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines …...

AbstractWe model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of …...

AbstractExtracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to …...