We discuss learning latent annotations for synchronous context-free grammars (SCFG) for the purpose of improving machine translation. We show that learning annotations for nonterminals results in not only more accurate translation, but also faster SCFG decoding.

We present a measure for evaluating Probabilistic Context Free Grammars (PCFG) based on their ambiguity resolution capabilities. Probabilities in a PCFG can be seen as a filtering mechanism: For an ambiguous sentence, the trees bearing maximum probability are single out, while all others are discarded. The level of ambiguity is related to the size of the singled out set of trees. Under our meas...

In this note it is proved that so called one-sided context-sensitive grammars can generate languages which cannot be generated by any context-free grammar. This fact is not quite new. It has been proved in [3], [4] and [5] (as far as the author knows). In [3] it is proved that a special one-sided context-sensitive grammar suggested by Dr. Fris ([1]) generates a language {a m b'"c"; Hn^ m} which...

In this paper, we present a hybrid grammar formalism designed to learn structured models of natural iconic gesture performances that allow for compressed representation and robust recognition. We analyze a dataset of iconic gestures and show how the proposed Featurebased Stochastic Context-Free Grammar (FSCFG) can generalize over both structural and feature-based variations among different gest...

This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with “adaptors” that can induce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichle...

We present a novel method for the computation of prefix probabilities for linear context-free rewriting systems. Our approach streamlines previous procedures to compute prefix probabilities for context-free grammars, synchronous context-free grammars and tree adjoining grammars. In addition, the methodology is general enough to be used for a wider range of problems involving, for example, sever...

We prove an analog of Parikh’s theorem for weighted context-free grammars over commutative, idempotent semirings, and exhibit a stochastic context-free grammar with behavior that cannot be realized by any stochastic right-linear context-free grammar. Finally, we show that every unary stochastic context-free grammar with polynomially-bounded ambiguity has an equivalent stochastic right-linear co...

We prove an analog of Parikh’s theorem for weighted context-free grammars over commutative, idempotent semirings, and exhibit a stochastic context-free grammar with behavior that cannot be realized by any stochastic right-linear context-free grammar. Finally, we show that every unary stochastic context-free grammar with polynomially-bounded ambiguity has an equivalent stochastic right-linear co...

There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016). This work extends this depth-bounding approach to probabilistic context-free grammar induction (DB-PCFG), which has a smaller parameter space than hierarchical sequence ...