Rule-Based Clause-Level Morphology for Multiple Languages (2022)

Abstract: This paper describes an approach for the morphosyntactic analysis of clauses, including the analysis of composite verb forms and both overt and covert pronouns. The approach uses grammatical rules for verb inflection and clause-internal word agreement to compute a clause’s morphosyntactic features from the morphological features of the individual words. The approach is tested for eight languages in the 1st Shared Task on Multilingual Clause-Level Morphology, where it achieves F1 scores between 79% and 99% (94% in average).

## introduction until recently the prediction of clause-level morphological / morphosyntactic features has been approached for a few individual languages only (see @xcite for czech, @xcite for hindi, @xcite for italian, @xcite for english, french and german, @xcite for english revisited, and @xcite for german revisited). most of the approaches are rule-based, first of all because annotated training data barely exists. on the other hand, it seems intuitive to approach this task in a rule-based manner, since morphosyntax follows strict grammatical rules (as opposed to heuristics) that can be implemented by a linguist. the first work to our knowledge which considers multiple and typologically diverse languages at a time is that of @xcite , who presents a cross-linguistic algorithm for compositeverb analysis and implements it for 11 languages, but refrains from evaluating the approach due to the lack of annotated gold data. the 1st shared task on multilingual clause-level morphology tackles this lack of data and provides data sets for eight typologically diverse languages. we re-implement and extend @xcite 's algorithm for the shared task (section 3), evaluate it (section 4) and discuss its advantages and shortcomings (section 5). the 1st shared task on multilingual clause-level morphology (task 3 analysis) provides data sets for eight languages. training sets (10,000 samples each) and development sets (2,000 samples each) for six languages were released first, and test sets (1,000 samples each) as well as all sets for two surprise languages (spanish and swahili) were released two weeks before the system submission deadline. each sample consists of a short sentence and a gold analysis. the sentence consists of a single clause and contains one verb form that can be simple (e.g. he looks) or composite (e.g. he had not been looking) as well as pronouns, adpositions and a sentence-final punctuation mark. the gold analysis consists of the main verb's lemma, the analysis of the verb form and the analyses of all pronouns, both overtly expressed pronouns (as in he looks) and covertly expressed ones (as in ∅ look!). the analyses are represented with uni-morph features @xcite . the task was to predict an analysis for an input sentence. since the test sets were provided without gold analyses, the submission and evaluation of systems was performed via codalab. foot_0 3 method ## evaluation and results for the evaluation, the gold analysis and the predicted analysis are decomposed into features. for example, the analysis ind;pst;pfv;nom(3,pl,masc);acc(1,pl, masc);neg;q is decomposed into the features φ = {ind, pst, pfv, nom-3, nom-pl, nom-masc, acc-1, acc-pl, acc-masc, neg, q}. given the features for the gold analysis φ g and for the predicted analysis φ p , the f1 score for one sample is calculated as follows: if the predicted lemma matches the gold lemma and s ℓ = 0 otherwise, and w ℓ = 3. while the development and training sets always contain only one gold analysis per sample, the test sets contain multiple gold analyses for samples with an ambiguous sentence. in case of such ambiguous sentences, the predicted analysis is compared to each gold analysis and the highest f1 score is chosen. the f1 for a data set (e.g. for the english test set) is the average f1 over all samples in that set. the results using our method are shown in for completeness, we also show the accuracies in terms of exact matches, i.e. the percentage of predicted analyses that exactly match a gold analysis (including ordering of the features), in ## discussion the main advantage of the presented method is probably the performance, although there is naturally some room for improvement. the second major advantage of the method is that it does not require any training data. this makes it a promising option for analyzing every language where manually annotated gold data is not available. no training also means that no training bias can be induced by the data, which arguably makes the method's performance more stable across text domains. in terms of languages, the algorithm is relatively universally applicable since the underlying mechanisms of inflection and agreement are the same across natural languages. this is also indicated by the performance that is very similar across languages and language families. 18 however, the method is not without shortcomings, all of which are clearly visible in the case of swahili. first of all, the method requires a (wordlevel) morphological analysis and a parser for the language to analyze. we decided to use unimorph because the output format in the shared task also uses unimorph features. @xcite , on the other hand, does not only use the parser but also the morphological analyzer that can be trained by spacy on a ud treebank. 19 the current version of the ud treebanks includes treebanks for 130 languages and 61 languages are listed as possible future extensions-swahili being one of them-, and unimorph currently provides resources for 167 languages. nonetheless, the current lack of both a treebank and morphological resources for swahili forced us to implement a workaround resulting in a much lower performance compared to the other languages. another drawback of our method is that knowledge about the grammar of the language to analyze is required to set-up the language-specific inflection table, the list of auxiliary verbs, the wordorder parameter (ov vs. vo), and in the current implementation also a list of words of negation as well as unimorph-style entries for pronouns and adpositions. @xcite already mentions that the study of composite verb forms in a foreign language can be extensive, but it is also prone to errors. it may be a coincidence that the languages with the best performance (english, french, german) are those languages which the author of this paper has the profoundest knowledge of, but it may also be due to the incomplete knowledge about the other languages acquired in the short term. although the algorithm is designed to be language-independent (with language-specific operations being controlled through the aforementioned parameters), its performance can be sometimes improved by languagespecific special rules (e.g. the rules for participle agreement in french), which again can only be implemented by someone who has the according knowledge of the language. ## conclusion we presented a method to predict clause-level morphological / morphosyntactic features. the main advantages are its performance (94% f1 on average), that it does not require training data and that it is applicable for multiple languages. the disadvantages are that it requires a preceding word-level morphological analysis, linguistic knowledge about the language to analyze and some time to set-up the method for a new language. while the implementation within the frame of the shared task is not applicable for general use (mainly because of the pre-and postprocessing), interested readers may want to have a look at the implementation from @xcite .