Smelting Gold and Silver for Improved Multilingual AMR -to- T ext Generation (2021)

Abstract: Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. We find that combining both complementary sources of information further improves multilingual AMR-to-text generation. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.

## introduction amr-to-text generation is the task of recovering a text with the same meaning as a given abstract meaning representation (amr) @xcite , and has recently received much research interest @xcite @xcite @xcite . amr has applications to a range of nlp tasks, including summarization @xcite and spoken language understanding @xcite , and has the potential power of acting as an interlingua that allows the generation of text in many different languages @xcite . while previous work has predominantly focused on monolingual english settings @xcite , recent work has also studied multilinguality in meaning representations @xcite . whereas damonte and cohen (2018) demonstrate 1 our code and checkpoints are available at https://github.com/ukplab/m-amr2text . ## related work approaches for amr-to-text generation predominantly focus on english, and typically employ an encoder-decoder architecture, employing a linearized representation of the graph @xcite . recently, models based on the graph-to-text paradigm @xcite improve over linearized approaches, explicitly encoding the amr structure with a graph encoder @xcite @xcite @xcite . advances in multilingual amr parsing have focused on a variety of different languages such as brazilian portuguese, chinese, czech and spanish @xcite @xcite . in contrast, little work has focused on the reverse amr-to-text setting @xcite . we aim to close this gap by experimenting with different data augmentation methods for efficient multilingual amr-to-text generation. ## multilingual amr-to-text generation in amr-to-text generation, we transduce an amr graph g to a surface realization as a sequence of tokens @xmath0, . . . , y |y| . as input we use an english-centric amr graph where the output y can be realized in different languages (see figure 1 ). ## experiments we implement our models using mt5 base from hug-gingface @xcite . we use the adafactor optimizer @xcite and employ a linearly decreasing learning rate schedule without warm-up. the hyperparameters we tune include the batch size, number of epochs and learning 4 https://tatoeba.org/ 5 https://github.com/ukplab/sentencetransformers/tree/master/docs/datasets 6 the english sentences of the parallel corpus are parsed using a state-of-the-art amr parser @xcite . rate. 7 the models are evaluated in the multilingual ldc2020t07 test set, using bleu @xcite , meteor @xcite , chrf++ @xcite and bertscore (zhang et al., 2020) metrics. we compare with a mt baseline -we generate the test set with an amr-to-english model trained with t5 @xcite and translate the generated english sentences to the target language using mt. for a fair comparison, our mt model is based on mt5 and trained with the same data as the other approaches. ## conclusion the unavailability of gold training data makes multilingual amr-to-text generation a challenging topic. we have extensively evaluated data augmentation methods by leveraging existing resources, namely a set of gold english amr-to-text data and a corpus of multilingual parallel sentences. our experiments have empirically validated that both sources of silver data -silver amr with gold sentences and gold amr with silver sentences -are complementary, and a combination of both leads to state-of-the-art performance on multilingual amrto-text generation tasks. tianyi zhang, varsha kishore, felix wu, kilian q. weinberger, and yoav artzi. 2020. bertscore: evaluating text generation with bert. in 8th international conference on learning representations, iclr 2020, addis ababa, ethiopia, april 26-30, 2020. huaiyu zhu, yunyao li, and laura chiticariu. 2019. towards universal semantic representation. in proceedings of the first international workshop on designing meaning representations, pages 177-181, florence, italy. association for computational linguistics.