AMR Dependency Parsing with a Typed Semantic Algebra

We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.Comment: This paper will be presented at ACL 2018 (see https://acl2018.org/programme/papers/

Constraint programming in computational linguistics

Constraint programming is a programming paradigm that was originally invented in computer science to deal with hard combinatorial problems. Recently, constraint programming has evolved into a technology which permits to solve hard industrial scheduling and optimization problems. We argue that existing constraint programming technology can be useful for applications in natural language processing. Some problems whose treatment with traditional methods requires great care to avoid combinatorial explosion of (potential) readings seem to be solvable in an efficient and elegant manner using constraint programming. We illustrate our claim by two recent examples, one from the area of underspecified semantics and one from parsing

Solving Unrestricted Dominance Graphs

We present the first ever algorithm for solving unrestricted dominance graphs. The algorithm extends existing polynomial solvers for restricted classes of dominance graphs, which are not sufficient to model newer theories of scope ambiguity. Using the new solver, these theories now have access to an efficient solver for the first time. The solver runs in cubic time; for those graph classes that could be solved in the past, the runtime is reduced to the same quadratic time that the most efficient previous solvers achieved

Sentence Generation as a Planning Problem

In this paper, we translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to tap into the recent performance improvements in off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action

Simple and effective data augmentation for compositional generalization

Compositional generalization, the ability to predict complex meanings from training on simpler sentences, poses challenges for powerful pretrained seq2seq models. In this paper, we show that data augmentation methods that sample MRs and backtranslate them can be effective for compositional generalization, but only if we sample from the right distribution. Remarkably, sampling from a uniform distribution performs almost as well as sampling from the test distribution, and greatly outperforms earlier methods that sampled from the training distribution. We further conduct experiments to investigate the reason why this happens and where the benefit of such data augmentation methods come from

Relating dominance formalisms

We establish for the first time a formal relationship between dominance graphs, used for modeling semantics, and grammar formalisms with underspecified dominance links, used for modeling syntax. We present a translation of normal dominance graphs into Unordered Vector Grammars with Dominance Links (UVG-DL) and prove that the configurations of the dominance graph correspond to the derivation trees of the grammar. Moreover, the standard algorithms for both formalisms compute isomorphic charts

Sponsoring, brand value and social media

The increasing involvement of individuals in social media over the past decade has enabled firms to pursue new avenues in communication and sponsoring activities. Besides general research on either social media or sponsoring, questions regarding the consequences of a joint activity (sponsoring activities in social media) remain unexplored. Hence, the present study analyses whether the perceived image of the brand and the celebrity endorser credibility of a top sports team influence the perceived brand value of the sponsoring firm in a social media setting. Moreover, these effects are compared between existing customers and non-customers of the sponsoring firm. Interestingly, perceived celebrity endorser credibility plays no role in forming brand value perceptions in the case of the existing customers. Implications for marketing theory and practice are derived. (authors' abstract