Multilingual AMR-to-Text Generation

Fan, Angela; Gardent, Claire

doi:10.18653/v1/2020.emnlp-main.231

Cited by 19 publications

(27 citation statements)

References 43 publications

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“…The results also show that our XLPT-AMR T-S models greatly advance the state of art. For example, our XLPT-AMR T-S models outperform Sheth et al (2021) by 3.4∼7.8 Smatch F1 on AMR parsing of the three languages while surpass Fan and Gardent (2020) by around 10 BLEU on AMR-to-text generation.…”

Section: Resultsmentioning

confidence: 60%

“…Finally, we compare our approach to the previous studies. Among them, both Blloshmi et al (2020) and Fan and Gardent (2020) adopt pretrained models which cover either the encoder part, or the decoder part. From the results we can see even our baseline Baseline pre-trained outperforms them by pre-training the encoder and the decoder simultaneously.…”

Section: Resultsmentioning

confidence: 99%

“…This data is designed for use in cross-lingual AMR research. Following Fan and Gardent (2020), we only evaluate on languages of German, Spanish and Italian where we have training data from EUROPARL. For AMR parsing evaluation, we utilize Smatch and other fine-grained metrics Damonte et al, 2017).…”

Section: Pre-training and Fine-tuning Model Settingsmentioning

confidence: 99%

“…Sheth et al (2021) explore annotation projection to leverage existing English AMR and overcome resource shortage in the target language. Furthermore, Fan and Gardent (2020) explore cross-lingual AMR-to-text based on pre-trained cross-lingual language model (XLM) (Lample and Conneau, 2019). In this paper we build strong cross-lingual pre-trained models for both AMR parsing and AMR-to-text generation.…”

Section: Related Workmentioning

confidence: 99%

“…Blloshmi et al (2020) follow the same line and create large-scale silver data to boost the performance of cross-lingual AMR parsing. Fan and Gardent (2020) focus on multilingual AMR-to-text generation for twenty one different languages. The aforementioned studies consider AMR parsing and AMR-to-text generation separately.…”

mentioning

confidence: 99%

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XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation

Xu¹,

Zhu

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Due to the scarcity of annotated data, Abstract Meaning Representation (AMR) research is relatively limited and challenging for languages other than English. Upon the availability of English AMR dataset and English-to-X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation. Specifically, we consider three types of relevant tasks, including AMR parsing, AMR-to-text generation, and machine translation. We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages. With properly pretrained models, we explore four different finetuning methods, i.e., vanilla fine-tuning with a single task, one-for-all MTL fine-tuning, targeted MTL fine-tuning, and teacher-studentbased MTL fine-tuning. Experimental results on AMR parsing and text generation of multiple non-English languages demonstrate that our approach significantly outperforms a strong baseline of pre-training approach, and greatly advances the state of the art. In detail, on LDC2020T07 we have achieved 70.45%, 71.76%, and 70.80% in Smatch F1 for AMR parsing of German, Spanish, and Italian, respectively, while for AMR-to-text generation of the languages, we have obtained 25.69, 31.36, and 28.42 in BLEU respectively. We make our code available on github https:// github.com/xdqkid/XLPT-AMR.

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Section: Resultsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 99%