Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer

By Marko Vidoni, Ivan Vulic, Goran Glavas. 2020

28 citations recorded by the source corpus.

Adapter modules, additional trainable parameters that enable efficient fine-tuning of pretrained transformers, have recently been used for language specialization of multilingual transformers, improving downstream zero-shot cross-lingual transfer. In this work, we propose orthogonal language and task adapters (dubbed orthoadapters) for cross-lingual transfer. They are trained to encode language- and task-specific information that is complementary (i.e., orthogonal) to the knowledge already stored in the pretrained transformer's parameters. Our zero-shot cross-lingual transfer experiments, involving three tasks (POS-tagging, NER, NLI) and a set of 10 diverse languages, 1) point to the usefulness of orthoadapters in cross-lingual transfer, especially for the most complex NLI task, but also 2) indicate that the optimal adapter configuration highly depends on the task and the target language. We hope that our work will motivate a wider investigation of usefulness of orthogonality constraints in language- and task-specific fine-tuning of pretrained transformers.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

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Citations in the corpus

AdapterDrop: On the Efficiency of Adapters in Transformers (2020, background): “…been shown to work well for machine translation (Bapna and Firat, 2019), cross-lingual transfer (Pfeiffer et al., 2020b, 2021b; Üstün et al., 2020; Vidoni et al., 2020; Ansell et al., 2021), community QA (Rücklé et al., 2020), and task composition for transfer learning (Stickland and Murray,…”
AdapterFusion: Non-Destructive Task Composition for Transfer Learning (2020, background): “, 2020), and cross-lingual transfer (Pfeiffer et al., 2020c,d; Üstün et al., 2020; Vidoni et al., 2020).”
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts (2020, background): “lingual setups, the idea is to increase the multilingual model capacity by storing language-specific knowledge of each language in dedicated parameters (Pfeiffer et al., 2020b; Vidoni et al., 2020).”
A Primer on Pretrained Multilingual Language Models (2021, unknown): “Vidoni et al. (2020) use orthogonality constraints to ensure that the language and task speciﬁc adapters learn complementary information.”
What to Pre-Train on? Efficient Intermediate Task Selection (2021, background): “2 Besides being more efficient, adapters are also highly modular, enabling a wider range of transfer learning techniques (Pfeiffer et al., 2020b, 2021a,b; Üstün et al., 2020; Vidoni et al., 2020; Rust et al., 2021; Ansell et al., 2021).”