The Proper Treatment of Verbal Idioms in G erman Discourse Representation Structure Parsing (2025)

Abstract: Existing datasets for semantic parsing lack adequate representations of potentially idiomatic expressions (PIEs), i.e., expressions consisting of two or more lexemes that can occur with either a literal or an idiomatic reading. As a result, we cannot test semantic parsers for their ability to correctly distinguish between the two cases, and to assign appropriate meaning representations. We address this situation by combining two semantically annotated resources to obtain a corpus of German sentences containing literal and idiomatic occurrences of PIEs, paired with meaning representations whose concepts and roles reflect the respective literal or idiomatic meaning. Experiments with a state-of-the-art semantic parser show that given appropriate training data, it can learn to predict the idiomatic meanings and improve performance also for literal readings, even though predicting the correct concepts in context remains challenging. We provide additional insights through evaluation on synthetic data.

## introduction meaning representations such as minimal recursion semantics @xcite , abstract meaning representations @xcite or discourse representation structures @xcite form a link between natural language and the realm of symbolic computation, including ontologies and logical reasoning. they have uses in tasks such as information extraction, dialogue systems, and computer-assisted study of natural language semantics @xcite . meaning representations have traditionally been constructed from text using rule-based precision grammars or combinations of statistical syntactic parsers and rule-based interpretation systems @xcite . more recently, larger quantities of annotated sentencemeaning pairs have made it possible to perform ac-decomposable verbal idiom: don't spill the beans! literal occurrence of a verbal potentially idiomatic expression: they like playing games on the playstation 2. datasets that have been constructed using computational grammars typically have a more or less strong built-in assumption that each occurrence of a content word is associated with exactly one occurrence of a concept (i.e., of a word sense from an ontology such as wordnet; @xcite . furthermore, one typically assumes that while lex-emes can be ambiguous, their senses do not depend on co-occurrence with specific other lexemes. these assumptions break down in the case of phrasemes or multiword expressions (mwes), i.e., combinations of two or more words expressing a single sense (e.g., pull someone's leg: kid.v.01), or being associated with different but specific senses when occurring together (e.g., spill the beans: talk.v.04 and secret.n.01). mwes occur in a variety of forms @xcite . in this paper, we focus on verbal mwes, i.e., mwes whose syntactic head is a verb. in particular, we focus on the subtype of verbal idiom. @xcite define verbal idioms (vids) as mwes with at least two lexicalized components including the head verb and at least one of its dependents, excluding special cases like light verb constructions, verb-particle constructions, inherently adpositional verbs or inherently reflexive verbs. following @xcite , we further distinguish two subtypes of verbal idioms: decomposable vids such as spill the beans where the lexicalized components still have individual meanings even though they are specific to the combination, and nondecomposable vids such as pull someone's leg where all lexical components express a single concept together. note also that even when two or more lexemes can form a vid together, they can still occur in the same syntactic configuration with a literal, non-idiomatic, compositionally derivable meaning. for example, the phrase playing games can occur with an idiomatic but also with a literal meaning. we are therefore dealing with potentially idiomatic expressions (pies; haagsma, 2020) with both idiomatic and literal occurrences. it should be noted that pie occurrences need not be contiguous but exhibit syntactic flexibility as in the beans were spilled or the games that we played. in the context of semantic parsing, pies present specific challenges: 1) on encountering a pie, the parser has to decide whether it indeed has the idiomatic meaning in this context, and 2) if so, it must produce the correct meaning representation, meaning one or more concepts that are specific to the idiom, and no additional concepts for additional components of non-decomposable idoms (see figure 1 ). in this paper, we demonstrate that existing semantic parsers for discourse representation structures underperform on sentences containing literal and idiomatic pies. we also show a way to remedy this situation. to this end, we combine two semantically annotated resources, the parallel meaning bank (pmb; @xcite and the dataset of @xcite , to obtain a corpus of german sentences containing literal and idiomatic occurrences of pies, annotated with meaning representations that reflect the correct meaning in context (section 2). we then show that enriching the training data of a drs parser with such data improves its performance on sentences containing idiomatic occurrences of pies, and in some cases its performance overall. nevertheless, it remains challenging for the parser to reliably distinguish between literal and idiomatic uses, and also to choose the correct concepts for idioms (section 3). we provide further insights with an evaluation on synthetic data (section 4). we conclude in section 5. besides these experimental designs and findings, our contributions include several reusable datasets which will be released upon publication, including an adjudicated version of ehren et al.'s semantically annotated idiom dataset, an accordingly reannotated version of sentences containing pies in the parallel meaning bank, and a synthetic dataset containing the annotated idioms isolated in canonical form, annotated with meaning representations. ## targeted training on pie instances we assess the performance of a seq2seq parser on pies, comparing four different training conditions: training on the unmodified pmb data (baseline), adding available pie instances into the training data (enhanced), adding a balanced mix of literal and idiomatic pie instances into the training data (bal- er [experiencer] schwimmt [buck.v.02] gegen [] den [] strom [stimulus] [trend.n.01] "he bucks the trend" (lit. "he swims against the tide") male.n.02 % er schwimmt.v.01buck.v.02 agentexperiencer -1 time +1 locationstimulus +2 % schwimmt gegen den time.n.08 equ now % tide.n.01 % strom. sie steckt [despair.v.01] [experiencer] den [] kopf [] in [] den [] sand [] "she despairs" (lit. "she puts the head into the sand") female.n.02 % sie steckt.v.01despair.v.01 agentexperiencer -1 time +1 theme +3 location +4 % steckt time.n.08 equ now % female.n.01 % den head.n.01 participant -1 % kopf in den sand.n.01 % sand. the meaning representations are discourse representation structures in sequence notation (cf. section 2.1). struck out (underlined) spans represent parts of the meaning representation that were removed (added) compared to the original pmb data. note that some of the replaced senses, such as steckt.v.01 or schwimmt.v.01, would be incorrect even in a literal reading, since they are not wordnet senses but artifacts of the bootstrapping process for the german drs data. ## evaluation on synthetic data the data in i and l is based on bronze and silver documents in the pmb and thus contains errors, even though concepts and roles representing the meanings of idioms have been automatically fixed in i. the above experiments thus give a somewhat misleading view of the parsers' performance. to better understand the performance on idioms without any unrelated error sources, we perform an evaluation on a synthetic 'test set' of minimal sentences containing idioms, paired with drss that are correct by construction, assuming the idiomatic reading.