From LFG To UD : A Combined Approach (2020)

Abstract: This paper reports on a systematic approach for deriving Universal Dependencies from LFG structures. The conversion starts with a step-wise transformation of the c-structure, combining part-of-speech (POS) information and the embedding path to determine the true head of dependency structures. The paper discusses several issues faced by existing algorithms when applied on Wolof and presents the strategies used to account for these issues. An experimental evaluation indicated that our approach was able to generate the correct output in more than 90% of the cases, leading to a substantial improvement in conversion accuracy compared to the previous models.

## introduction this paper describes a methodology to automatically convert lexical-functional grammar (lfg) @xcite into universal dependencies (ud) structures @xcite . previous studies in this field @xcite @xcite show disagreement regarding the structure to start from for the conversion. @xcite proposed a lifting algorithm which performs a step-wise transformation of the c-(onstituent) structure into a dependency tree. in contrast, the p&p algorithm (for lack of a better name) proposed by @xcite takes f-(unctional) structure as the basis for constructing dependency structures. these algorithms faced several issues when applied to wolof (see section 3). accordingly, this paper presents a new approach (discussed in section 4) that combines and extends previous methods. in lfg, c-structure characterizes the phrase structure tree configurations and f-structure encodes grammatical relations (e.g. subject, object) and features (e.g. person, number). for instance, the wolof lfg grammar (dione, 2014) coupled with the xerox linguistic environment (xle) @xcite assigns to the sentence in (1) the c-and simplified f-structure in figures 1 and 2 . fig. 2: f-structure of (1) the f-structure states that the main predicate of (1) is taal 'start' and it has two arguments: a subject (subj) and an object (obj). each of these arguments has its own semantic predicate (e.g. sofoor 'driver' for subj) and a det feature embedded under spec introduced by the determiner bi. nonterminal nodes in c-structure are mapped to particular substructures in f-structure via the φ (phi) function. thus, in the c-structure in fig. 1 , the leftmost nodes dp, np, n and d all map to the substructure with index 13 (the value of subj) in the corresponding f-structure. likewise, the rightmost nodes dp, np, n and d all map to the substructure with index 5 (i.e., the value of obj). all the other nonterminals, including ip, s, vp, v' and cl map to the entire f-structure with index 0. a crucial concept is the functional head (fh) @xcite . in lfg, a 'head' of a phrase shares its features with its mother. a solid line between a mother node and its daughter node (dn) in c-structure indicates that dn is a fh of its mother, i.e. they project to the same f-structure. ## from lfg to dependency structures our approach is based on modified versions of both the lifting @xcite and the p&p algorithms @xcite , which are briefly presented in sections 2.1 and 2.2, respectively. ## issues with existing algorithms when applied on the wolof data, the existing algorithms faced various issues as discussed below. ## the combined approach our approach is based on a modified and extended version of existing algorithms. first, the c-structure is transformed step-wise by recursively replacing each non-terminal node by its functional head(s). then, both the embedding path (as suggested by the lifting algorithm) and the head selection procedure (as suggested by the p&p algorithm) are used to determine the true head. the underlying assumptions of these algorithms were modified where necessary to account for the issues outlined in section 3. ## evaluation to develop our model, we used a relatively small wolof corpus that contains 510 sentences: (i) 50 sentences distributed within the pargram group @xcite , and (ii) 460 sentences drawn from short stories @xcite . these data were annotated with the wolof lfg grammar (dione, 2020) and disambiguated using the lfg parsebanker @xcite . this tool supports disambiguation based on discriminants @xcite . the disambiguated parses in prolog format were converted to a tigerxml @xcite file used to generate the c-structure tree and the f-structure graph. to test our model, we used a different set of 453 sentences extracted from the wolof lfg treebank (dione, 2014). these sentences are then annotated semi-automatically in ud v.2 by means of udpipe @xcite . subsequent to this, the annotations were corrected manually using ud annotatrix @xcite to ensure parsing quality. the final annotations provide the gold standard set used to assess the performance of the combined approach compared to the lifting and p&p algorithms. ## conclusion this paper has proposed a new approach to automatic conversion of lfg structures to dependency structures. this approach extends existing algorithms, using their inter-agreement as a validation mechanism. it suggests careful reconsideration of the concept of embedding path to better account for c-structure nodes that lack a pred value at f-structure. this is particularly true for morphologically rich languages. likewise, adapting the pos based head selection procedure suggested by the p&p algorithm proved to be essential for selecting the true dependency head. using f-structure information is crucial for modeling language-specific phenomena (e.g. free relatives in wolof) that are analyzed following a particular choice of the language's grammar. the combined approach also revisited the concept of structure sharing to enable the analysis of dislocated constituents.