Extending Multilingual Machine Translation through Imitation Learning

• URL: http://arxiv.org/abs/2311.08538v1
• Date: Tue, 14 Nov 2023 21:04:03 GMT
• Title: Extending Multilingual Machine Translation through Imitation Learning
• Authors: Wen Lai, Viktor Hangya, Alexander Fraser
• Abstract summary: Imit-MNMT treats the task as an imitation learning process, which mimicks the behavior of an expert. We show that our approach significantly improves the translation performance between the new and the original languages. We also demonstrate that our approach is capable of solving copy and off-target problems.
• Score: 60.15671816513614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the growing variety of languages supported by existing multilingual neural machine translation (MNMT) models, most of the world's languages are still being left behind. We aim to extend large-scale MNMT models to a new language, allowing for translation between the newly added and all of the already supported languages in a challenging scenario: using only a parallel corpus between the new language and English. Previous approaches, such as continued training on parallel data including the new language, suffer from catastrophic forgetting (i.e., performance on other languages is reduced). Our novel approach Imit-MNMT treats the task as an imitation learning process, which mimicks the behavior of an expert, a technique widely used in the computer vision area, but not well explored in NLP. More specifically, we construct a pseudo multi-parallel corpus of the new and the original languages by pivoting through English, and imitate the output distribution of the original MNMT model. Extensive experiments show that our approach significantly improves the translation performance between the new and the original languages, without severe catastrophic forgetting. We also demonstrate that our approach is capable of solving copy and off-target problems, which are two common issues existence in current large-scale MNMT models.

Related papers

• Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation [103.90963418039473]
  Bi-ACL is a framework that uses only target-side monolingual data and a bilingual dictionary to improve the performance of the MNMT model. We show that Bi-ACL is more effective both in long-tail languages and in high-resource languages.
  arXiv  Detail & Related papers  (2023-05-22T07:31:08Z)
• Neural Machine Translation For Low Resource Languages [0.0]
  This paper investigates the realm of low resource languages and builds a Neural Machine Translation model to achieve state-of-the-art results. The paper looks to build upon the mBART language model and explore strategies to augment it with various NLP and Deep Learning techniques.
  arXiv  Detail & Related papers  (2023-04-16T19:27:48Z)
• Learning to translate by learning to communicate [11.43638897327485]
  We formulate and test a technique to use Emergent Communication (EC) with a pre-trained multilingual model to improve on modern Unsupervised NMT systems. In our approach, we embed a multilingual model into an EC image-reference game, in which the model is incentivized to use multilingual generations to accomplish a vision-grounded task. We present two variants of EC Fine-Tuning (Steinert-Threlkeld et al., 2022), one of which outperforms a backtranslation-only baseline in all four languages investigated.
  arXiv  Detail & Related papers  (2022-07-14T15:58:06Z)
• Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning [48.15259834021655]
  We present a pragmatic approach towards building a multilingual machine translation model that covers hundreds of languages. We use a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting.
  arXiv  Detail & Related papers  (2022-01-09T23:36:44Z)
• Continual Learning in Multilingual NMT via Language-Specific Embeddings [92.91823064720232]
  It consists in replacing the shared vocabulary with a small language-specific vocabulary and fine-tuning the new embeddings on the new language's parallel data. Because the parameters of the original model are not modified, its performance on the initial languages does not degrade.
  arXiv  Detail & Related papers  (2021-10-20T10:38:57Z)
• Cross-lingual Transferring of Pre-trained Contextualized Language Models [73.97131976850424]
  We propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate TRILayer" structure. We show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency.
  arXiv  Detail & Related papers  (2021-07-27T06:51:13Z)
• ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora [21.78571365050787]
  ERNIE-M is a new training method that encourages the model to align the representation of multiple languages with monolingual corpora. We generate pseudo-parallel sentences pairs on a monolingual corpus to enable the learning of semantic alignment between different languages. Experimental results show that ERNIE-M outperforms existing cross-lingual models and delivers new state-of-the-art results on various cross-lingual downstream tasks.
  arXiv  Detail & Related papers  (2020-12-31T15:52:27Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation [81.7786241489002]
  Massively multilingual models for neural machine translation (NMT) are theoretically attractive, but often underperform bilingual models and deliver poor zero-shot translations. We argue that multilingual NMT requires stronger modeling capacity to support language pairs with varying typological characteristics. We propose random online backtranslation to enforce the translation of unseen training language pairs.
  arXiv  Detail & Related papers  (2020-04-24T17:21:32Z)

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.