Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (2021)

Abstract: We propose a transition-based bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. Bubble representations were proposed in the formal linguistics literature decades ago; they enhance dependency trees by encoding coordination boundaries and internal relationships within coordination structures explicitly. In this paper, we introduce a transition system and neural models for parsing these bubble-enhanced structures. Experimental results on the English Penn Treebank and the English GENIA corpus show that our parsers beat previous state-of-the-art approaches on the task of coordination structure prediction, especially for the subset of sentences with complex coordination structures.

## introduction coordination structures are prevalent in treebank data @xcite , especially in long sentences @xcite , and they are among the most challenging constructions for nlp models. difficulties in correctly identifying coordination structures have consistently contributed to a significant portion of errors in stateof-the-art parsers @xcite @xcite . these errors can further propagate to downstream nlp modules and applications, and limit their performance and utility. for example, @xcite report that missing conjuncts account for two-thirds of the errors in recall made by their open information extraction system. coordination constructions are particularly challenging for the widely-adopted dependency-based paradigm of syntactic analysis, since the asymmetric definition of head-modifier dependency relations is not directly compatible with the symmetric 1 code at github.com/tzshi/bubble-parser-acl21. bubble tree: i prefer hot coffee or tea and a bun conj cc conj amod conj cc conj det nsubj obj ud tree: i prefer hot coffee or tea and a bun nsubj obj amod conj cc conj cc det figure 1 : bubble tree and (basic) ud tree for the same example sentence. (for clarity, we omit punctuation and single-word bubble boundaries.) bubbles explicitly encode the scope of the shared modifier hot with respect to the nested coordination, whereas the ud tree gives both tea and bun identical relationships to hot. in this paper, we explore @xcite alternative solution: extend the dependency-tree representation by introducing bubble structures to explicitly encode coordination boundaries. the coheads within a bubble enjoy a symmetric relationship, as befits a model of conjunction. further, bubble trees support representation of nested coordination, with the scope of shared modifiers identifiable by the attachment sites of bubble arcs. figure 1 compares a bubble tree against a universal dependencies (ud; @xcite ) tree for the same sentence. yet, despite theses advantages, implementation of the formalism was not broadly pursued, for reasons unknown to us. given its appealing and intuitive treatment of coordination phenomena, we revisit the bubble tree formalism, introducing and implementing a transition-based solution for parsing bubble trees. our transition system, bubble-hybrid, extends the arc-hybrid transition system @xcite with three bubble-specific transitions, each corresponding to opening, expanding, and closing bubbles. we show that our transition system is both sound and complete with respect to projective bubble trees (defined in § 2.2). experiments on the english penn treebank (ptb; @xcite extended with coordination annotation @xcite ) and the english genia treebank @xcite demonstrate the effectiveness of our proposed transition-based bubble parsing on the task of coordination structure prediction. our method achieves state-of-the-art performance on both datasets and improves accuracy on the subset of sentences exhibiting complex coordination structures. ## our transition system for parsing bubble trees although, as we have seen, bubble trees have theoretical benefits in representing coordination structures that interface with an overall dependencybased analysis, there has been a lack of parser implementations capable of handling such representations. in this section, we fill this gap by introducing a transition system that can incrementally build projective bubble trees. transition-based approaches are popular in dependency parsing @xcite . we propose to extend the arc-hybrid transition system @xcite with transitions specific to bubble structures. foot_3 ## models our model architecture largely follows that of @xcite neural arc-hybrid parser, but we additionally introduce feature composition for non-singleton bubbles, and a rescoring module to reduce frequent coordinationboundary prediction errors. our model has five components: feature extraction, bubble-feature composition, transition scoring, label scoring, and boundary subtree rescoring. ## conclusion we revisit @xcite bubble tree representations for explicitly encoding coordination boundaries as a viable alternative to existing mechanisms in dependency-based analysis of coordination structures. we introduce a transition system that is both sound and complete with respect to the subclass of projective bubble trees. empirically, our bubble parsers achieve state-of-the-art results on the task of coordination structure prediction on two english datasets. future work may extend the research scope to other languages, graph-based, and non-projective parsing methods.