XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques

Abstract

Meaning Representation (AMR) is a popular formalism of natural language that represents the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings from strings and for this reason it lends itself well to the encoding of semantics across languages. However, cross-lingual AMR parsing is a hard task, because training data are scarce in languages other than English and the existing English AMR parsers are not directly suited to being used in a cross-lingual setting. In this work we tackle these two problems so as to enable cross-lingual AMR parsing: we explore different transfer learning techniques for producing automatic AMR annotations across languages and develop a crosslingual AMR parser, XL-AMR. This can be trained on the produced data and does not rely on AMR aligners or source-copy mechanisms as is commonly the case in English AMR parsing. The results of XL-AMR significantly surpass those previously reported in Chinese, German, Italian and Spanish. Finally we provide a qualitative analysis which sheds light on the suitability of AMR across languages. We release XL-AMR at github.com/SapienzaNLP/xlamr.

Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach

“…Zero-shot Cross-Lingual Results. Having parity in the quality and quantity of data across languages is often an unrealistic expectation, especially whenever the task requires expert annotators (Pasini, 2020): this is the reason why, over the last few years, cross-lingual transfer learning techniques and benchmarks have garnered attention in NLP (Barba et al, 2020;Blloshmi et al, 2020;Hu et al, 2020). While transfer learning techniques are becoming increasingly popular, their application to SRL is not straightforward.…”

MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

“…Wang et al (2018) parsed Chinese AMR with a transition-based system. For cross-lingual AMR parsing, Blloshmi et al (2020) trained an AMR parser similar to the approach of Zhang et al (2019b), using cross-lingual transfer learning, outperforming the transition-based cross-lingual AMR parser of Damonte and Cohen (2018) on German, Spanish, Italian, and Chinese.…”

Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations

“…Evaluated across multiple multilingual and cross-lingual Semantic Word Similarity datasets, Conception shows state-of-the-art results not only compared to concept representations such as NASARI, but also to multilingual word embeddings such as Conceptnet Numberbatch and cross-lingual language models such as XLM. Additionally, our concept representations are particularly robust on resource-poor languages, like Farsi, along the lines of recent work in Semantic Parsing and Semantic Role Labeling aimed at bridging the gap between languages (Blloshmi et al, 2020;. Finally, Conception can be seamlessly applied to a downstream task: in Word Sense Disambiguation, it improves over state-of-the-art supervised and knowledge-based sense embeddings, showing that Conception encodes information that is still not captured by BERT-based contextualized representations.…”