DCU / TCD - FORG e at W eb NLG ’23: I rish rules! ( W eg NLG 2023) (2023)

Abstract: In this paper, we describe the submission of Dublin City University (DCU) and Trinity College Dublin (TCD) for the WebNLG 2023 shared task. We present a fully rule-based pipeline for generating Irish texts from DBpedia triple sets which comprises 4 components: triple lexicalisation, generation of noninflected Irish text, inflection generation, and post-processing.

## introduction the webnlg dataset is a benchmark for data-totext natural language generation (nlg) consisting of {input, output} pairs, where the input is a set of n triples (1 ≤ @xmath0. the triples are extracted from dbpedia and represent relationships between dbpedia resources, namely subjects (db-subj) and objects (db-obj), via respective properties e.g. for the property country: texas | country | united_states. the webnlg'23 shared task focuses on four languages for which the existing resources are limited: irish, welsh, breton and maltese; we submitted irish outputs only. large language models are becoming more and more used for nlg, but it is well known that they are heavily dependent on the quantity and quality of data they are trained on. on the other hand, rule-based systems although limited in terms of coverage and/or fluency are usually easier to adapt to low-resource languages. for our dcu/tcd-forge submission, our pipeline con-sists of 4 fully rule-based modules, which are described in the remainder of the paper: lexicalisation of input triples: instantiation of predicateargument templates with db-subj and db-obj values (section 2); generation of non-inflected irish sentences: a sequence of graph transducers that progressively specify the linguistic structures into their surface-oriented form (forge, section 3); generation of morphological inflections: finite-state transducers to produce inflections via two-level morphology (section 4); and postprocessing: cleaning and formatting of the text (section 5). dcu/tcd-forge uses disk space of ∼8mb and runs with less than 1gb of ram; it generates the webnlg test set (1,779 texts) in ∼15 min (∼0.5 sec/text) and achieved 0.167 bleu. ## lexicalisation of input triples in this section, we detail the steps involved in the lexicalisation of the webnlg inputs: we start with the predicate-argument (predarg) templates and their instantiation (2.1), and continue with the adaptations carried out for irish generation (2.2). ## generation of non-inflected sentences in this section, we describe forge (3.1) and provide an overview of its extensions for irish (3.2). ## generation of morphological inflections we describe here the morphology generation (4.1) and its interface with forge (4.2). ## post-processing the post-processing consists of regular expressions to revert reserved characters to their original form, true-case and clean the texts, and take care of prefixing, hyphenation, contraction, lenition and eclipsis phenomena triggered by the inflected forms of words; see appendix a for an example. ## discussion and conclusion we have presented dcu/tcd-forge, a fully rulebased pipeline of four modules for irish text generation at webnlg'23; sample inputs and outputs for all modules are provided in appendix a. the bleu score provided by the organisers foot_6 (0.167) is significantly lower than forge's scores on english at webnlg'20 (0.406 @xcite ); this is likely because we created our lexicalisations without reference to train and dev irish texts, i.e. surface similarity is likely to be low. however, the gap in bleu scores between our system (0.167) and the highest-scoring gpt-based system (0.204) @xcite is less than 0.04 points; this compares to a corresponding gap of 0.13 points between forge and the best english system at webnlg'20. we will be able to draw more reliable conclusions when the results of the human evaluation are released. figure 3: lexicalisation output: instantiated predarg templates forge morphology post-processing bí+verb+presind tá tá agra_airport+noun+masc+com+sg agra_airport agra airport , , , reáchtáilte reáchtáilte reáchtáilte ag ag ag indian_air_force+noun+masc+com+sg indian_air_force indian air force , , , lonnaithe+adj+masc+com+sg lonnaithe lonnaithe i i ins an_india+noun+masc+com+sg an_india an india . . . bí+verb+presind tá tá viag+noun+masc+com+sg viag viag i i in a a a aitheantóir+noun+masc+com+sg aitheantóir aitheantóir suímh suímh suímh icao+noun+masc+com+sg icao icao .