Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation

• URL: http://arxiv.org/abs/2208.05852v1
• Date: Thu, 11 Aug 2022 14:42:42 GMT
• Title: Language Tokens: A Frustratingly Simple Approach Improves Zero-Shot Performance of Multilingual Translation
• Abstract summary: We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper proposes a simple yet effective method to improve direct (X-to-Y) translation for both cases: zero-shot and when direct data is available. We modify the input tokens at both the encoder and decoder to include signals for the source and target languages. We show a performance gain when training from scratch, or finetuning a pretrained model with the proposed setup. In the experiments, our method shows nearly 10.0 BLEU points gain on in-house datasets depending on the checkpoint selection criteria. In a WMT evaluation campaign, From-English performance improves by 4.17 and 2.87 BLEU points, in the zero-shot setting, and when direct data is available for training, respectively. While X-to-Y improves by 1.29 BLEU over the zero-shot baseline, and 0.44 over the many-to-many baseline. In the low-resource setting, we see a 1.5~1.7 point improvement when finetuning on X-to-Y domain data.

Related papers

• sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting [29.63634707674839]
  We introduce a novel recipe for creating a multilingual synthetic instruction tuning dataset, sPhinX. sPhinX is created by selectively translating instruction response pairs from English into 50 languages. We test the effectiveness of sPhinx by using it to fine-tune two state-of-the-art models, Mistral-7B and Phi-Small.
  arXiv  Detail & Related papers  (2024-07-13T13:03:45Z)
• Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation [79.96416609433724]
  Zero-shot translation (ZST) aims to translate between unseen language pairs in training data. The common practice to guide the zero-shot language mapping during inference is to deliberately insert the source and target language IDs. Recent studies have shown that language IDs sometimes fail to navigate the ZST task, making them suffer from the off-target problem.
  arXiv  Detail & Related papers  (2023-09-28T17:02:36Z)
• Strategies for improving low resource speech to text translation relying on pre-trained ASR models [59.90106959717875]
  This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST) We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
  arXiv  Detail & Related papers  (2023-05-31T21:58:07Z)
• Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models [95.32691891392903]
  In this paper, we do cross-lingual evaluation on various NLU tasks using prompt-tuning and compare it with fine-tuning. The results show that prompt tuning achieves much better cross-lingual transfer than fine-tuning across datasets.
  arXiv  Detail & Related papers  (2022-10-22T05:48:02Z)
• Adapting to Non-Centered Languages for Zero-shot Multilingual Translation [12.487990897680422]
  We propose a simple, lightweight yet effective language-specific modeling method by adapting to non-centered languages. Experiments with Transformer on IWSLT17, Europarl, TED talks, and OPUS-100 datasets show that our method can easily fit non-centered data conditions.
  arXiv  Detail & Related papers  (2022-09-09T06:34:12Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages [75.08199398141744]
  We present AmericasNLI, an extension of XNLI (Conneau et al.), to 10 indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.62%.
  arXiv  Detail & Related papers  (2021-04-18T05:32:28Z)
• Self-Learning for Zero Shot Neural Machine Translation [13.551731309506874]
  This work proposes a novel zero-shot NMT modeling approach that learns without the now-standard assumption of a pivot language sharing parallel data. Compared to unsupervised NMT, consistent improvements are observed even in a domain-mismatch setting.
  arXiv  Detail & Related papers  (2021-03-10T09:15:19Z)
• Multilingual Speech Translation with Efficient Finetuning of Pretrained Models [82.22294901727933]
  A minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability. Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model.
  arXiv  Detail & Related papers  (2020-10-24T08:15:08Z)

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.