In NMT we search for the mode of the model distribution to form predictions. The mode and
other high-probability translations found by beam search have been shown to often be …

In NMT we search for the mode of the model distribution to form predictions. The mode and
other high-probability translations found by beam search have been shown to often be …

Despite success in many domains, neural models struggle in settings where train and test
examples are drawn from different distributions. In particular, in contrast to humans …

Incremental syntactic parsing has been an active research area both for cognitive scientists
trying to model human sentence processing and for NLP researchers attempting to combine …

Steedman proposes as a formal universal of natural language grammar that grammatical
permutations of the kind that have given rise to transformational rules are limited to a class …

In this paper we explore the novel idea of building a single universal reordering model from
English to a large number of target languages. To build this model we exploit typological …

We study the relationship between word order freedom and preordering in statistical
machine translation. To assess word order freedom, we first introduce a novel entropy …

Latent-variable probabilistic context-free grammars are latent-variable models that are
based on context-free grammars. Nonterminals are associated with latent states that provide …

Long-range word order differences are a well-known problem for machine translation.
Unlike the standard phrase-based models which work with sequential and local phrase …

Semantic parsing is the task of translating natural language utterances onto machine-
interpretable programs, which can be executed against a real-world environment to obtain …