Exploring Neural Methods for Parsing Discourse Representation Structures

Exploring Neural Methods for Parsing Discourse Representation Structures Rik van Noord Lasha Abzianidze Antonio Toral Johan Bos Center for Language and Cognition, University of Groningen {r.i.k.van.noord, l.abzianidze, a.toral.ruiz, johan.bos}@rug.nl Abstract Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of pro- ducing meaning representations based on formal semantics. We present a sequence- to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming tradi- tional DRS parsers. To facilitate the learn- ing of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as in- put and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further. 1 Introduction Semantic parsing is the task of mapping a natu- ral language expression to an interpretable mean- ing representation. Semantic parsing used to be the domain of symbolic and statistical approaches (Pereira and Shieber, 1987; Zelle and Mooney, 1996; Blackburn and Bos, 2005). Recently, how- ever, neural methods, and in particular sequence- to-sequence models, have been successfully applied to a wide range of semantic parsing tasks. These include code generation (Ling et al., 2016), question answering (Dong and Lapata, 2016; He and Golub, 2016) and Abstract Meaning Repre- sentation parsing (Konstas et al., 2017). Because these models have no intrinsic knowledge of the structure (tree, graph, set) they have to produce, recent work also focused on structured decod- ing methods, creating neural architectures that always output a graph or a tree (Alvarez-Melis and Jaakkola, 2017; Buys and Blunsom, 2017). These methods often outperform the more general sequence-to-sequence models but are tailored to specific meaning representations. This paper will focus on parsing Discourse Representation Structures (DRSs) proposed in Discourse Representation Theory (DRT), a well- studied formalism developed in formal semantics (Kamp, 1984; Van der Sandt, 1992; Asher, 1993; Kamp and Reyle, 1993; Muskens, 1996; Van Eijck and Kamp 1997; Kadmon, 2001; Asher and Las-carides, 2003), dealing with many semantic phenomena: quantifiers, negation, scope ambi- guities, pronouns, presuppositions, and discourse structure (see Figure 1). DRSs are recursive struc- tures and thus form a challenge for sequence-to- sequence models because they need to generate a well-formed structure and not something that looks like one but is not interpretable. The problem that we try to tackle bears similarities to the recently introduced task of map- ping sentences to an Abstract Meaning Represen- tation (AMR; Banarescu et al. 2013). But there are notable differences between DRS and AMR. Firstly, DRSs contain scope, which results in a more linguistically motivated treatment of modals, quantification, and negation. Secondly, DRSs con- tain a substantially higher number of variable bindings (reentrant nodes in AMR terminol- ogy), which are challenging for learning (Damonte et al., 2017). DRS parsing …

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