Lightweight Adapter Tuning for Multilingual Speech Translation

• URL: http://arxiv.org/abs/2106.01463v1
• Date: Wed, 2 Jun 2021 20:51:42 GMT
• Title: Lightweight Adapter Tuning for Multilingual Speech Translation
• Authors: Hang Le, Juan Pino, Changhan Wang, Jiatao Gu, Didier Schwab, Laurent Besacier
• Abstract summary: Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. This paper proposes a comprehensive analysis of adapters for multilingual speech translation.
• Score: 47.89784337058167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adapter modules were recently introduced as an efficient alternative to fine-tuning in NLP. Adapter tuning consists in freezing pretrained parameters of a model and injecting lightweight modules between layers, resulting in the addition of only a small number of task-specific trainable parameters. While adapter tuning was investigated for multilingual neural machine translation, this paper proposes a comprehensive analysis of adapters for multilingual speech translation (ST). Starting from different pre-trained models (a multilingual ST trained on parallel data or a multilingual BART (mBART) trained on non-parallel multilingual data), we show that adapters can be used to: (a) efficiently specialize ST to specific language pairs with a low extra cost in terms of parameters, and (b) transfer from an automatic speech recognition (ASR) task and an mBART pre-trained model to a multilingual ST task. Experiments show that adapter tuning offer competitive results to full fine-tuning, while being much more parameter-efficient.

Related papers

• Efficient Adapter Finetuning for Tail Languages in Streaming Multilingual ASR [44.949146169903074]
  The heterogeneous nature and imbalanced data abundance of different languages may cause performance degradation. Our proposed method brings 12.2% word error rate reduction on average and up to 37.5% on a single locale.
  arXiv  Detail & Related papers  (2024-01-17T06:01:16Z)
• Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters [20.168480824057923]
  A popular approach to creating a cross-language retrieval model is to substitute a monolingual pretrained language model in the retrieval model. We show that models trained with monolingual data are more effective than fine-tuning the entire model when transferring to a Cross Language Information Retrieval setting.
  arXiv  Detail & Related papers  (2022-12-20T17:25:04Z)
• Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation [129.99918589405675]
  Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Multilingual fine-tuning improves performance on low-resource languages but requires modifying the entire model and can be prohibitively expensive. We propose training language-family adapters on top of mBART-50 to facilitate cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-09-30T05:02:42Z)
• Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval [66.69799641522133]
  State-of-the-art neural (re)rankers are notoriously data hungry. Current approaches typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. We show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer.
  arXiv  Detail & Related papers  (2022-04-05T15:44:27Z)
• Multilingual Unsupervised Neural Machine Translation with Denoising Adapters [77.80790405710819]
  We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data. For this problem the standard procedure so far to leverage the monolingual data is back-translation, which is computationally costly and hard to tune. In this paper we propose instead to use denoising adapters, adapter layers with a denoising objective, on top of pre-trained mBART-50.
  arXiv  Detail & Related papers  (2021-10-20T10:18:29Z)
• Efficient Test Time Adapter Ensembling for Low-resource Language Varieties [115.12997212870962]
  Specialized language and task adapters have been proposed to facilitate cross-lingual transfer of multilingual pretrained models. An intuitive solution is to use a related language adapter for the new language variety, but we observe that this solution can lead to sub-optimal performance. In this paper, we aim to improve the robustness of language adapters to uncovered languages without training new adapters.
  arXiv  Detail & Related papers  (2021-09-10T13:44:46Z)
• Serial or Parallel? Plug-able Adapter for multilingual machine translation [15.114588783601466]
  We propose PAM, a Transformer model augmented with defusion adaptation for multilingual machine translation. PAM consists of embedding and layer adapters to shift the word and intermediate representations towards language-specific ones. Experiment results on IWSLT, OPUS-100, and WMT benchmarks show that method outperforms several strong competitors.
  arXiv  Detail & Related papers  (2021-04-16T14:58:28Z)
• MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer [136.09386219006123]
  We propose MAD-X, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages. MAD-X outperforms the state of the art in cross-lingual transfer across a representative set of typologically diverse languages on named entity recognition and causal commonsense reasoning.
  arXiv  Detail & Related papers  (2020-04-30T18:54:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.