Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications

• URL: http://arxiv.org/abs/2403.17528v1
• Date: Tue, 26 Mar 2024 09:31:55 GMT
• Title: Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications
• Authors: Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa, Hideyuki Tachibana, Yotaro Watanabe,
• Abstract summary: We introduce Multilingual Sentence T5 (m-ST5) as a larger model of NLI-based multilingual sentence embedding. By employing the low-rank adaptation (LoRA) technique, we have achieved a successful scaling of the model's size to 5.7 billion parameters. It was particularly noteworthy that languages with fewer resources or those with less linguistic similarity to English benefited more from the parameter increase.
• Score: 4.240899165468488
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conventional methods. However, the potential benefits from the recent ``exponential'' growth of language models with billions of parameters have not yet been fully explored. In this paper, we introduce Multilingual Sentence T5 (m-ST5), as a larger model of NLI-based multilingual sentence embedding, by extending Sentence T5, an existing monolingual model. By employing the low-rank adaptation (LoRA) technique, we have achieved a successful scaling of the model's size to 5.7 billion parameters. We conducted experiments to evaluate the performance of sentence embedding and verified that the method outperforms the NLI-based prior approach. Furthermore, we also have confirmed a positive correlation between the size of the model and its performance. It was particularly noteworthy that languages with fewer resources or those with less linguistic similarity to English benefited more from the parameter increase. Our model is available at https://huggingface.co/pkshatech/m-ST5.

Related papers

• Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It) [2.6150740794754155]
  We investigate whether fine-tuning a T5 model on an intermediate task that predicts structural linguistic properties of sentences modifies its performance in the target task of predicting sentence-level complexity. Results obtained for both languages and in cross-lingual configurations show that linguistically motivated intermediate fine-tuning has generally a positive impact on target task performance, especially when applied to smaller models and in scenarios with limited data availability.
  arXiv  Detail & Related papers  (2024-02-27T15:34:15Z)
• MAPLE: Multilingual Evaluation of Parameter Efficient Finetuning of Large Language Models [7.321459642283822]
  Finetuning can improve the performance of language models without requiring massive resources and compute. We finetune LLama-2-7B and Mistral-7B models on two synthetic multilingual instruction tuning datasets to determine its effect on model performance. We find that PEFT of smaller open-source models sometimes bridges the gap between the performance of these models and the larger ones, however, English performance can take a hit.
  arXiv  Detail & Related papers  (2024-01-15T11:06:43Z)
• On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model [49.81429697921861]
  We study the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models. We show that prompt tuning is more effective in enhancing the performance of low-resource languages than fine-tuning.
  arXiv  Detail & Related papers  (2023-11-14T00:43:33Z)
• UniMax: Fairer and more Effective Language Sampling for Large-Scale Multilingual Pretraining [92.3702056505905]
  We propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases.
  arXiv  Detail & Related papers  (2023-04-18T17:45:50Z)
• Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning [99.42850643947439]
  We show that going beyond English-centric bitexts, coupled with a novel sampling strategy, substantially boosts performance across model sizes. Our XY-LENT XL variant outperforms XLM-RXXL and exhibits competitive performance with mT5 XXL while being 5x and 6x smaller respectively.
  arXiv  Detail & Related papers  (2022-10-26T17:16:52Z)
• PaLM: Scaling Language Modeling with Pathways [180.69584031908113]
  We trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.
  arXiv  Detail & Related papers  (2022-04-05T16:11:45Z)
• Language Models are Few-shot Multilingual Learners [66.11011385895195]
  We evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones.
  arXiv  Detail & Related papers  (2021-09-16T03:08:22Z)
• Probing Multilingual Language Models for Discourse [0.0]
  We find that the XLM-RoBERTa family of models consistently show the best performance. Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations.
  arXiv  Detail & Related papers  (2021-06-09T06:34:21Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)

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.