Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer
- URL: http://arxiv.org/abs/2404.16627v1
- Date: Thu, 25 Apr 2024 14:10:52 GMT
- Title: Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer
- Authors: Jianyu Zheng, Fengfei Fan, Jianquan Li,
- Abstract summary: We present a novel framework called "Lexicon-Syntax Enhanced Multilingual BERT"
We use Multilingual BERT as the base model and employ two techniques to enhance its learning capabilities.
Our experimental results demonstrate this framework can consistently outperform all baselines of zero-shot cross-lingual transfer.
- Score: 4.944761231728674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised cross-lingual transfer involves transferring knowledge between languages without explicit supervision. Although numerous studies have been conducted to improve performance in such tasks by focusing on cross-lingual knowledge, particularly lexical and syntactic knowledge, current approaches are limited as they only incorporate syntactic or lexical information. Since each type of information offers unique advantages and no previous attempts have combined both, we attempt to explore the potential of this approach. In this paper, we present a novel framework called "Lexicon-Syntax Enhanced Multilingual BERT" that combines both lexical and syntactic knowledge. Specifically, we use Multilingual BERT (mBERT) as the base model and employ two techniques to enhance its learning capabilities. The code-switching technique is used to implicitly teach the model lexical alignment information, while a syntactic-based graph attention network is designed to help the model encode syntactic structure. To integrate both types of knowledge, we input code-switched sequences into both the syntactic module and the mBERT base model simultaneously. Our extensive experimental results demonstrate this framework can consistently outperform all baselines of zero-shot cross-lingual transfer, with the gains of 1.0~3.7 points on text classification, named entity recognition (ner), and semantic parsing tasks. Keywords:cross-lingual transfer, lexicon, syntax, code-switching, graph attention network
Related papers
- HC$^2$L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding [45.12153788010354]
State-of-the-art model for cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning.
We propose Hybrid and Cooperative Contrastive Learning to address this problem.
arXiv Detail & Related papers (2024-05-10T02:40:49Z) - mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view
Contrastive Learning [54.523172171533645]
Cross-lingual named entity recognition (CrossNER) faces challenges stemming from uneven performance due to the scarcity of multilingual corpora.
We propose Multi-view Contrastive Learning for Cross-lingual Named Entity Recognition (mCL-NER)
Our experiments on the XTREME benchmark, spanning 40 languages, demonstrate the superiority of mCL-NER over prior data-driven and model-based approaches.
arXiv Detail & Related papers (2023-08-17T16:02:29Z) - VECO 2.0: Cross-lingual Language Model Pre-training with
Multi-granularity Contrastive Learning [56.47303426167584]
We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments.
Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs.
token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
arXiv Detail & Related papers (2023-04-17T12:23:41Z) - Multi-level Contrastive Learning for Cross-lingual Spoken Language
Understanding [90.87454350016121]
We develop novel code-switching schemes to generate hard negative examples for contrastive learning at all levels.
We develop a label-aware joint model to leverage label semantics for cross-lingual knowledge transfer.
arXiv Detail & Related papers (2022-05-07T13:44:28Z) - Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence
Encoders [85.80950708769923]
We probe multilingual language models for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs.
We also devise a novel method to expose this knowledge by additionally fine-tuning multilingual models.
We report substantial gains on standard benchmarks.
arXiv Detail & Related papers (2022-04-30T13:23:16Z) - 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) - VECO: Variable and Flexible Cross-lingual Pre-training for Language
Understanding and Generation [77.82373082024934]
We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages.
It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language.
The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
arXiv Detail & Related papers (2020-10-30T03:41:38Z) - InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language
Model Pre-Training [135.12061144759517]
We present an information-theoretic framework that formulates cross-lingual language model pre-training.
We propose a new pre-training task based on contrastive learning.
By leveraging both monolingual and parallel corpora, we jointly train the pretext to improve the cross-lingual transferability of pre-trained models.
arXiv Detail & Related papers (2020-07-15T16:58:01Z)
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.