DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning

• URL: http://arxiv.org/abs/2403.04158v1
• Date: Thu, 7 Mar 2024 02:30:46 GMT
• Title: DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning
• Authors: Ling Ge, Chunming Hu, Guanghui Ma, Jihong Liu, Hong Zhang
• Abstract summary: Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. We propose a Disentangled and Adaptive Network (DA-Net) to address these challenges.
• Score: 11.78085199896157
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. Existing methods typically focus on weighting the predictions produced by language-specific classifiers of different sources that follow a shared encoder. However, all source languages share the same encoder, which is updated by all these languages. The extracted representations inevitably contain different source languages' information, which may disturb the learning of the language-specific classifiers. Additionally, due to the language gap, language-specific classifiers trained with source labels are unable to make accurate predictions for the target language. Both facts impair the model's performance. To address these challenges, we propose a Disentangled and Adaptive Network (DA-Net). Firstly, we devise a feedback-guided collaborative disentanglement method that seeks to purify input representations of classifiers, thereby mitigating mutual interference from multiple sources. Secondly, we propose a class-aware parallel adaptation method that aligns class-level distributions for each source-target language pair, thereby alleviating the language pairs' language gap. Experimental results on three different tasks involving 38 languages validate the effectiveness of our approach.

Related papers

• Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis [18.25948580496853]
  Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages. This paper studies whether the typological similarity between source and target languages impacts the performance of cross-lingual transfer.
  arXiv  Detail & Related papers  (2024-04-09T15:35:41Z)
• AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection [69.79627042058048]
  AdaCCD is a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages.
  arXiv  Detail & Related papers  (2023-11-13T12:20:48Z)
• Adapting the adapters for code-switching in multilingual ASR [10.316724084739892]
  Large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance. This formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. We propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network.
  arXiv  Detail & Related papers  (2023-10-11T12:15:24Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Reducing language context confusion for end-to-end code-switching automatic speech recognition [50.89821865949395]
  We propose a language-related attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model. By calculating the respective attention of multiple languages, our method can efficiently transfer language knowledge from rich monolingual data.
  arXiv  Detail & Related papers  (2022-01-28T14:39:29Z)
• Multilingual Transfer Learning for Code-Switched Language and Speech Neural Modeling [12.497781134446898]
  We address the data scarcity and limitations of linguistic theory by proposing language-agnostic multi-task training methods. First, we introduce a meta-learning-based approach, meta-transfer learning, in which information is judiciously extracted from high-resource monolingual speech data to the code-switching domain. Second, we propose a novel multilingual meta-ems approach to effectively represent code-switching data by acquiring useful knowledge learned in other languages. Third, we introduce multi-task learning to integrate syntactic information as a transfer learning strategy to a language model and learn where to code-switch.
  arXiv  Detail & Related papers  (2021-04-13T14:49:26Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)
• Inducing Language-Agnostic Multilingual Representations [61.97381112847459]
  Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. We examine three approaches for this: (i) re-aligning the vector spaces of target languages to a pivot source language; (ii) removing language-specific means and variances, which yields better discriminativeness of embeddings as a by-product; and (iii) increasing input similarity across languages by removing morphological contractions and sentence reordering.
  arXiv  Detail & Related papers  (2020-08-20T17:58:56Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)

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.