Comparing UMR and Cross-lingual Adaptations of AMR (2023)

Abstract: Abstract Meaning Representation (AMR) is a popular semantic annotation schema that presents sentence meaning as a graph while abstracting away from syntax. It was originally designed for English, but has since been extended to a variety of non-English versions of AMR. These cross-lingual adaptations, to varying degrees, incorporate language-specific features necessary to effectively capture the semantics of the language being annotated. Uniform Meaning Representation (UMR) on the other hand, the multilingual extension of AMR, was designed specifically for cross-lingual applications. In this work, we discuss these two approaches to extending AMR beyond English. We describe both approaches, compare the information they capture for a case language (Spanish), and outline implications for future work.

## introduction abstract meaning representation (amr; @xcite is a symbolic meaning representation which captures the meaning of a sentence in the form of a directed, rooted graph composed of predicate argument structures. amr was originally designed for english, but has since been extended to many other languages. these cross-lingual adaptations of amr vary in their approach to adapting english-centric amr to other languages, which has posed a number of challenges. in addition to language-or language familyspecific @xcite adaptations of amr, uniform meaning representation (umr; @xcite is a recent multilingual extension of amr which attempts to be generally cross-lingually portable. approach to cross-lingual adaptation has a significant impact on the utility of the annotated data. formalisms which have similarly structured parallel annotations are better suited for incorporation sentence: he denied any wrongdoing. ## cross-lingual adaptations of amr amr is designed to abstract away from the surfaceform and syntactic nuance of the sentence, focusing only the basic meaning. in amr annotations, nodes reflect concepts and the edges are labeled with relations between the concepts. annotation of amr concepts relies in part on propbank lexicon of frame files 1 @xcite @xcite , by annotating the frame associated with the token as the concept in the amr graph. though amr was designed exclusively for english and was not intended to be an interlingua @xcite , it has now been extended to multiple languages. amr has also been assessed as an interlingua for czech @xcite , chinese @xcite , and spanish @xcite . @xcite explores the adaptability of english amr to czech and chinese. the authors suggest that, although it was not designed to be an interlingua, amr may be cross-linguistically adaptable because it abstracts away from morphosyntactic differences. crosslinguistic comparisons between english/czech and english/chinese amr pairs indicate that most pairs align well, though there are some instances of divergence due to insertions, for example. recent work has defined the types and causes of divergences between cross-lingual amr pairs for english-spanish parallel sentences. the causes of structural differences between parallel amrs are identified as being due to semantic divergences, syntactic divergences, or annotation choices @xcite . in the subsections that follow, we consider four adaptations of amr to individual languages. li et al. (2016) suggested that amr would be particularly well adapted to languages which vary morphosyntactically from english, because amr abstracts away from the surface syntactic structure, motivating adaptation to chinese. the chinese amr (camr) annotation schema largely matches that of the english annotation schema, with the concepts being tokens in chinese instead of english. notably, chinese has very little inflectional morphology, so the amr concepts more often directly correspond to tokens in the sentence than in english annotation. extensions to the annotation guidelines are made for chinese-specific constructions, including but not limited to (1) number and classifier construction, (2) serial-verb construction, (3) headless relative construction, (4) verb-complement construction, (5) split verb construction, and (6) reduplication. in the case where reduplication signals intensified meaning, chinese amr annotates this with another abstract concept, often with the role :unit. discourse relations are also represented with concepts from the chinese discourse treebank (dctb; zhou and @xcite . these adaptations to the guidelines were identified during the annotation process. ## umr the recent development of the uniform meaning representation @xcite aims to incorporate uniform treatments for linguistic diversity into the amr annotation process. uniform meaning representation (umr) is designed to extend amr to a cross-linguistically viable meaning representation. related work on babelnet meaning representation @xcite also extends amr to a multilingual context, by moving away from english propbank and instead using ver-batlas @xcite for cross-lingual frames and babelnet concept inventory @xcite . to accommodate cross-linguistic diversity, umr incorporates paradigmatic lattices to organize annotation categories from coarse-grained to more specific. annotators are able to use the degree of granularity that is most suitable for the grammar of the language being annotated. lattices produced for this purpose indicate degrees of granularity for discourse relations, modality, number, spatial relations, aspect, and temporality. the number of concepts associated with any given token (polysynthesis and agglutination) can also vary by language, so umr does not require that morphologically complex words be broken down into separate morphemes when being annotated as conceptshowever, it builds in the ability to do so where appropriate to support uniformity. umr extends amr in 3 core ways: (1) it is capable of annotating low-resource languages, (2) it more comprehensively annotates modality, aspect, quantification, and scope for the benefit of logical inference, and (3) it annotates temporal, modal, and coreference relations across sentences. at the sentence level, umr adds aspect, modal strength, and quantifier scope attribute roles. aspect is annotated for events and states at five base level values, with finer-grained values in lattice format (e.g., :aspect state). sentence-level modal annotation comes in three strengths for both affirmative and negative (e.g., :modstr prtaff for partial-affirmative). the optional scope node augments predicates. at the document level, umr adds temporal and modal dependencies, plus coreference. documentlevel semantic relations can be created for concepts/events within a sentence or across sentence boundaries. these document-level relations are able to be more fine-grained and provide more detailed information than their sentence-level counterparts, for instance, document-level modal relations are able to mark a conceiver in addition to the strength and polarity marked at the sentence level. while umr follows amr in using existing roleset lexicons where possible (referred to as stage 1 annotation), languages without these resources can also be annotated in umr (stage 0 annotation). during stage 0 annotation, umr-writer @xcite allows annotators to select tokens for use as graph predicates and then add those predicates into a lexicon. argument structures for these predicates are added using umr's inventory of participant and non-participant roles. the predicates added to the working lexicon in combination with their participant role annotation information can be used to generate a roleset lexicon, moving a language from stage 0 to stage 1 annotation. recent work on umr has produced small sets of annotations for four indigenous languages (kukama, arapaho, sanapaná, and navajo) @xcite , an online application (umr-writer) for producing amr annotations @xcite , automatically annotating tense and aspect in umr @xcite , and incorporating non-verbal interactions into umr annotation @xcite . @xcite outlined deterministic conversion of amrs to umrs, specifically the roles, rolesets, and concepts. ## differences between umr and cross-lingual amr in this section, we compare the specific linguistic features that both schemata encode, and consider two noteworthy obstacles/factors to successful annotation of umr or amr: idiomatic phrases and reliance on english concepts. ## challenges for umr & cross-lingual amr umr & cross-lingual amr face a number of challenges when adapting to various languages, most notably in the representation of idiomatic phrases. reliance on underlying lexicons leads to graph structural inconsistencies for parallel sentences. idiomatic phrases. idiomatic phrases are a challenge for cross-lingual amr/umr because of the relationship between a phrase's individual tokens and its overall meaning @xcite @xcite . even within a single language, it can be difficult for annotators to determine the best way to incorporate predicate argument structures associated with the specific combination of individual tokens (literal expression) and the argument feature spanish amr umr in-language verb senses modality grammatical number opted for simplicity pronoun drop not specified politeness affixes (third person clitic pronouns, suffixes) double negation same as single negation not specified document-level representation scope aspect se usage impersonal pronoun, reflexive pronouns, passive voice given that stage 0 umr permits annotation of tokens into multiple concepts (e.g. compound words) or of multiple tokens into a single concepts (e.g. multi-word concepts), we expect that an altered version of smatch @xcite will need to be adapted in order to successfully identify parallelism in meaning when quantitatively comparing umrs in different languages. reliance on english concepts. prior work has explored cross-lingual differences in parallel amrs and to what extent amr is an interlingua @xcite , and suggests that the amr annotation schema may be more compatible with certain languages than others (i.e. more compatible with chinese than czech). current cross-lingual adaptations of amr highlight this, because some cross-lingual guidelines require more changes to handle linguistic variation than others, though the structure of arguments and concepts remain largely unchanged. the approaches which use english abstract rolesets for the cross-lingual annotation (for example, accompany-01 as the reification for the :accompanier role) exhibit significant english bias because the arguments for concepts are determined by their english usage. amr adaptations vary in degree of reliance on english annotations and resources, ranging from simply working with the english amr guidelines as a baseline and extending them, to using english propbank for sense annotation @xcite or aligning english and portuguese sentences and translating english annotations to their cross-lingual framesets @xcite . a factor that has enabled cross-lingual amr extensions for individual languages is the existence of lexicons in those languages, such as propbanks. this is an obstacle to amr annotation for low-resource languages. because many meaning representations require additional resources to produce annotations, the lack of prior non-english resource work poses an issue for future non-english resource work @xcite . this issue has been handled by umr by developing a "road map" for annotation of low-resource languages @xcite . reliance on frame files. the quality/extent of the lexicon of rolesets available for a given language impacts amr/umr annotation. for example, spanish amr @xcite ) makes use of ancora @xcite , but despite being the most comprehensive publicly available lexical resource for spanish, it is limited in the senses it contains, so other adaptations of amr for spanish have opted against its use @xcite . thus, even with the "road map" for annotation of low-resource languages in umr, there are complexities caused by reliance on external resources that affect umr/amr annotation. spanish amr was forced to add a supplementary database of frame files / senses when using ancora, and stage 1 umr annotation will likely also need to provide additional resources when relying on external lexicons. the umr writer @xcite is designed to allow annotators to add lexical entries to the roleset lexicon file used for annotation as need arises during annotation, pairing the lexicon-development process with umr annotation. roleset development can be incredibly complicated, however-particularly for polysynthetic and agglutinating languages like arapaho-so this feature of the umr-writer is a vital first step out of many when it comes to establishing a robust lexical resource. ## conclusion cross-lingual adaptations of amr use the english annotation guidelines as a baseline, and then make a set of adaptations for linguistic features specific to the other language. the linguistic phenomena incorporated into each cross-linguistic adaptation also varies by language (as described in §2), because these phenomena are language-specific. we conclude that umr successfully handles the vast majority of even the more language-specific features of cross-lingual adaptations of amr. the challenges for umr annotation in need of further investigation and consideration include the development of quantitative metrics, which will need to account for umr's flexibility in multiword/affix annotation, and the complexities associated with the generation of roleset lexicons for low-resource languages. future work providing general insight into the morphosyntactic strategies of amr and umr might provide additional insight into their cross-lingual applicability.