AMR Alignment for Morphologically-rich and Pro-drop Languages (2022)

Abstract: Alignment between concepts in an abstract meaning representation (AMR) graph and the words within a sentence is one of the important stages of AMR parsing. Although there exist high performing AMR aligners for English, unfortunately, these are not well suited for many languages where many concepts appear from morpho-semantic elements. For the first time in the literature, this paper presents an AMR aligner tailored for morphologically-rich and pro-drop languages by experimenting on the Turkish language being a prominent example of this language group. Our aligner focuses on the meaning considering the rich Turkish morphology and aligns AMR concepts that emerge from morphemes using a tree traversal approach without additional resources or rules. We evaluate our aligner over a manually annotated gold data set in terms of precision, recall and F1 score. Our aligner outperforms the Turkish adaptations of the previously proposed aligners for English and Portuguese by an F1 score of 0.87 and provides a relative error reduction of up to 76%.

## background and related work in amr parsing, although the parser takes sentences as input and produces amr graphs, it is challenging to learn complete semantic representation from the sentences directly by using sentence-amr graph pairs. therefore, several parsing approaches need a word-concept alignment stage to know the semantic representation of words separately. one should note that concepts' names may differ substantially (e.g., due to inflections, derivations, or semantic closeness) from the related lexical words, and it is probable that they could not be mapped directly: for example, the word 'desirous' or 'desires' could be related to the concept name 'desire.01' in an amr graph. this situation may be even harder in morphologically rich languages, where the character length of the inflectional affixes could be longer then the length of the lemma. another example could be the word 'afraid' related to the concept 'fear-01'. an aligner is a tool that maps amr concepts to the related words within the sentence. the outputs of the aligner are used as input data to train the amr parsers. jamr @xcite aligner, the first amr aligner in the literature, is built on heuristic rules and greedy search. in this method, fuzzy matching between words and concepts is searched using heuristic rules. the aligner moves down from the first rule and looks for a fuzzy match based on the rule currently being processed. while some rules are applied to all nodes by traversing the entire graph for each rule, some are only applied to some specific nodes (e.g., entity names). the tamr @xcite aligner is an extension of the method presented in jamr with emphasis on meaning. the list of jamr rules has been expanded with syntactic and semantic matching, where semantic and morpho-semantic matching are used together with fuzzy matching. the connection between verb-invoking nouns and their verb frames (e.g., example -exemplify) is provided by the morphological meaning database @xcite . @xcite used syntax-based statistical machine translation with an unsupervised word alignment method in the alignment approach. during the alignment, they linearize amr graphs with the ibm word alignment model @xcite and map the nodes to english sentences. @xcite presents an amr aligner for portuguese which is a morphologically rich language. the authors solve the word-concept alignment using the word mover's distance @xcite and lexical lists for the alignment of the abstract concepts and entity names. the alignment format adopted in the literature is presented by jamr, where alignment blocks are separated with white space (figure 1 ). each alignment block includes a word span and its graph fragment where a pipe sign ('|') separates them. graph fragments consist of nodes represented with their position in the amr graphs. a root node is located at position 0 ('want-01'); its children take 0.x where x represents the order of the children. for example, the first child of the root node takes 0.0 as a position indicator ('boy'); the second takes 0.1 ('believe-01'). ## the aligner for turkish, an alignment approach depending only on word-concept matching @xcite does not fully cover all concepts. on the other hand, unsupervised machine translation approaches @xcite are not easily applicable due to representation issues @xcite and the need for high-volume of parallel data. in turkish, some of the correspondences are not present explicitly as a word, and are hidden inside the words as morphemes (e.g., personal markers, modality markers) due to its complex morphology and prodrop nature. consider the sentences in figure 2 "sana gelecegimi bilebilmene şaşırdım" (i am surprised that you could know that i would be coming to you.). the lemma of the words are 'sen' (you), 'gel'(come), 'bil' (know) and 'şaşır' (be shocked). the lexical concepts related to these may be aligned using fuzzy matching, however, the other concepts 'ben' (i) and mümkün.01 (possible-01)) deriving from their suffixes could not be matched. 'ben' is a dropped pronoun represented with a the first personal suffix (-m) that attach to verb lemma 'gel', 'mümkün.01' is originated from the modality marker @xcite . figure 2 shows the aligned version of the sentence "sana gelecegimi bilebilmene şaşırdım" (i am surprised that you could know that i would be coming to you.). to align such concepts, one alternative is to follow the literature and expand the rule list of jamr @xcite with the new rules to handle morphology based concepts. we believe this is not an option due to the following reasons: (i) there may be morphemes whose meaning can be changed according to the context. in order to align them, their meaning should be determined first and this needs semantic interpretation. modality marker (-meli) is such an example and could carry out different meanings (i.e., 'should' or 'have to') depending on the context. (ii) there may be morphemes that invoke predicates, and the predicates invoked by the same morphemes can be different based on the nouns being attached. for example, when the very productive suffix -ci (with surface forms ci, cı, çi, çı under different vowel harmonies) attaches to nouns, it may mean 1) 'a person who sells' the item given in the noun lemma (e.g., 'simitçi' is the person who sells bagels where 'simit' is bagel), 2) 'a person who runs' the item given in the noun lemma (e.g., 'lokantacı' is a person who runs a restaurant where 'lokanta' is restaurant) 3) 'a person who plays' (e.g., 'bascı' is a person who plays bass guitar where 'bas' is bass guitar), and so forth. covering all possible meanings with defining rules requires numerous rules. (iii) construction of a solution on top of the morphemes (i.e., aligning morphemes using a predefined list) requires a preliminary morphological analysis stage. the aligner would become very dependent on the performance of the morphological analysis, and its errors propagate throughout the alignment. considering these, we believe that a better approach should be proposed for the alignment of handling morphology-based concepts. we propose an alignment strategy which relies on the word-concept similarity and tree traversal. our aligner has two steps. in the first step, it builds a map where concepts are mapped to their word correspondence. this mapping is done by using similarity between pre-trained word embeddings for the words of the sentence and the node labels of the graph. the mapping does not necessarily include all concepts in this step: morphology derived and abstract concepts are left unmapped. the second step focuses on aligning all concepts. first, it starts aligning with concept-words pairs in the mapping obtained in the first step. then it aligns the remaining concepts (i.e., morphology derived and abstract concepts) by traversing the amr graph through the mapping. for each concept-word pair, the aligner visits neighbors of the concept by following the heuristically determined paths, and any unaligned neighbors are simply added to the alignments for the word. our aligner is detailed in the following subsections: section 3.1 and 3.2. ## experiments and results in order to evaluate the performance of our aligner, we randomly choose 100 sentences from a turkish amr corpus @xcite . and manually align the concepts of the amr graphs with the words within sentences. 7 following the same evaluation method used in jamr foot_7 , we compare our aligner with tamr @xcite , jamr @xcite , and portuguese-aligner (shortly, pramr) @xcite . we adapt these aligners to turkish. the predefined dictionaries such as months, conjunctions, etc., are replaced with their turkish counterparts, and fasttext is used in tamr and pramr. we set similarity thresholds as 0.5 and 1.5 respectively. since pramr uses lexical lists for named entities, we localized these as far as possible via a portuguese to turkish translator and adding additional turkish gazetteers. we design another experiment to evaluate our apas shown in we make a further error analysis to see the weaknesses of our aligner. one of our aligner's mistakes is the mismatch/unmatch of specific concepts. since our alignment algorithm greedily searches unmapped concepts, any mistakes in the mapping phase result in the wrong alignments. although our aligner uses the power of the pre-trained word embeddings, it fails to match the punctuation marks when they create concepts, especially the cases when they have a coordination role in the sentence: for example the comma mark is related to the concept 'and', and the colon mark is related to the concept 'de.01'(say.01), however these punctuation marks are not similar to the concept names neither semantically nor syntactically. the alignment of the light verbs is another unmatched case where our aligner fails. the turkish propbank ( şahin, 2016) represents them as frames of auxiliary verbs, which is how the amr uses them too. therefore, our aligner maps only the verb part due to semantic similarity; the first part of the verb is left unmatched. for example 'tercih et-' (to prefer) is represented with 'et.16' our aligner aligns only 'et' (do). our aligner also shows poor performance on the alignment of the auxiliary verb 'ol'. this verb has 26 frames, including the widespread meanings 'have' (ol.04) and 'become' (ol.03). when there are multiple occurrences within the same sentence, the aligner does not have enough information to distinguish these frames. as a result, it may produce wrong mappings. an option to solve this ambiguity problem could be to integrate propbank verb frames as an external resource in future works. one should note that this kind of additions would increase the alignment cost. ## conclusions and feature work in this paper, we proposed an alignment approach for morphologically-rich and pro-drop languages and presented the first amr aligner designed for turkish which is prominent language of morphologically rich languages. our aligner uses pretrained word vectors and fuzzy matching for aligning concrete concepts. furthermore, we present an algorithm for the alignment problem of concepts that emerged from the morphemes; this simple approach may be adopted to other morphologically rich and pro-drop languages with little effort. our study reveals the challenging points in the turkish alignment study, and we believe that our findings will accelerate the development of multilingual amr parsing studies. as a future work, we plan to expand our study on the other morphologically rich and pro-drop languages (e.g., portuguese).