We argue that multilingual parallel data provides a valuable source of indirect supervision for induction of shallow semantic representations .

Although in this work we focused primarily on improving performance for each individual language, crosslingual semantic representation could be extracted by a simple postprocessing step.

5This has been explored before for shallow semantic representations (Lang and Lapata, 2011a; Titov and Klementiev, 201 1).

The goal of this work is to show that parallel data is useful in unsupervised induction of shallow semantic representations .

Though syntactic representations are often predictive of semantic roles (Levin, 1993), the interface between syntactic and semantic representations is far from trivial.

However, most of this research has focused on induction of syntactic structures (Kuhn, 2004; Snyder et al., 2009) or morphologic analysis (Snyder and Barzilay, 2008) and we are not aware of any previous work on induction of semantic representations in the crosslingual setting.

Learning of semantic representations in the context of monolingual weakly-parallel data was studied in Titov and Kozhevnikov (2010) but their setting was semi-supervised and they experimented only on a restricted domain.

Semi-supervised and weakly-supervised techniques have also been explored for other types of semantic representations but these studies again have mostly focused on restricted domains (Kate and Mooney, 2007; Liang et al., 2009; Goldwasser et al., 2011; Liang et al., 2011).

The surface realisation algorithm extends the algorithm proposed in (Gardent and Perez—Beltrachini, 2010) and adapts it to work on the SR dependency input rather than on flat semantic representations .

Typically, the input to surface realisation is a structured representation (i.e., a flat semantic representation , a first order logic formula or a dependency tree) rather than a string.

Approaches based on reversible grammars (Carroll et al., 1999) have used the semantic formulae output by parsing to evaluate the coverage and performance of their realiser; similarly, (Gardent et al., 2010) developed a tool called GenSem which traverses the grammar to produce flat semantic representations and thereby provide a benchmark for performance and coverage evaluation.