Revisiting Language Encoding in Learning Multilingual Representations

• URL: http://arxiv.org/abs/2102.08357v1
• Date: Tue, 16 Feb 2021 18:47:10 GMT
• Title: Revisiting Language Encoding in Learning Multilingual Representations
• Authors: Shengjie Luo, Kaiyuan Gao, Shuxin Zheng, Guolin Ke, Di He, Liwei Wang, Tie-Yan Liu
• Abstract summary: We propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model. Experiments show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets.
• Score: 70.01772581545103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer has demonstrated its great power to learn contextual word representations for multiple languages in a single model. To process multilingual sentences in the model, a learnable vector is usually assigned to each language, which is called "language embedding". The language embedding can be either added to the word embedding or attached at the beginning of the sentence. It serves as a language-specific signal for the Transformer to capture contextual representations across languages. In this paper, we revisit the use of language embedding and identify several problems in the existing formulations. By investigating the interaction between language embedding and word embedding in the self-attention module, we find that the current methods cannot reflect the language-specific word correlation well. Given these findings, we propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. For a sentence, XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model to process with their language-specific meanings. In such a way, XLP achieves the purpose of appropriately encoding "language" in a multilingual Transformer model. Experimental results show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets. Codes and models will be released at https://github.com/lsj2408/XLP.

Related papers

• LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation [21.980770995466134]
  We introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages. This method involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling.
  arXiv  Detail & Related papers  (2024-02-18T07:24:34Z)
• Counterfactually Probing Language Identity in Multilingual Models [15.260518230218414]
  We use AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models. We find that, given a template in Language X, pushing towards Language Y systematically increases the probability of Language Y words.
  arXiv  Detail & Related papers  (2023-10-29T01:21:36Z)
• 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)
• Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across Languages [3.716965622352967]
  We propose new criteria to evaluate the quality of lexical representation and vocabulary overlap observed in sub-word tokenizers. Our findings show that the overlap of vocabulary across languages can be actually detrimental to certain downstream tasks.
  arXiv  Detail & Related papers  (2023-05-26T18:06:49Z)
• Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes [0.2538209532048867]
  A novel Orthogonal Structural Probe (Limisiewicz and Marevcek, 2021) allows us to answer this question for specific linguistic features. We evaluate syntactic (UD) and lexical (WordNet) structural information encoded inmBERT's contextual representations for nine diverse languages. We successfully apply our findings to zero-shot and few-shot cross-lingual parsing.
  arXiv  Detail & Related papers  (2021-09-10T15:03:11Z)
• 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)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)
• 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)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

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.