A Framework for Hierarchical Multilingual Machine Translation
- URL: http://arxiv.org/abs/2005.05507v1
- Date: Tue, 12 May 2020 01:24:43 GMT
- Title: A Framework for Hierarchical Multilingual Machine Translation
- Authors: Ion Madrazo Azpiazu, Maria Soledad Pera
- Abstract summary: This paper presents a hierarchical framework for building multilingual machine translation strategies.
It takes advantage of a typological language family tree for enabling transfer among similar languages.
Exhaustive experimentation on a dataset with 41 languages demonstrates the validity of the proposed framework.
- Score: 3.04585143845864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multilingual machine translation has recently been in vogue given its
potential for improving machine translation performance for low-resource
languages via transfer learning. Empirical examinations demonstrating the
success of existing multilingual machine translation strategies, however, are
limited to experiments in specific language groups. In this paper, we present a
hierarchical framework for building multilingual machine translation strategies
that takes advantage of a typological language family tree for enabling
transfer among similar languages while avoiding the negative effects that
result from incorporating languages that are too different to each other.
Exhaustive experimentation on a dataset with 41 languages demonstrates the
validity of the proposed framework, especially when it comes to improving the
performance of low-resource languages via the use of typologically related
families for which richer sets of resources are available.
Related papers
- xCoT: Cross-lingual Instruction Tuning for Cross-lingual
Chain-of-Thought Reasoning [36.34986831526529]
Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models.
We propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.
arXiv Detail & Related papers (2024-01-13T10:53:53Z) - T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text
Classification [50.675552118811]
Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest.
We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
arXiv Detail & Related papers (2023-06-08T07:33:22Z) - Decomposed Prompting for Machine Translation Between Related Languages
using Large Language Models [55.35106713257871]
We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations.
We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
arXiv Detail & Related papers (2023-05-22T14:52:47Z) - Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help? [29.01386302441015]
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages.
The performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer.
We propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer.
arXiv Detail & Related papers (2021-10-15T02:31:48Z) - Adaptive Sparse Transformer for Multilingual Translation [18.017674093519332]
A known challenge of multilingual models is the negative language interference.
We propose an adaptive and sparse architecture for multilingual modeling.
Our model outperforms strong baselines in terms of translation quality without increasing the inference cost.
arXiv Detail & Related papers (2021-04-15T10:31:07Z) - Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting.
Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
arXiv Detail & Related papers (2021-03-24T16:20:02Z) - Towards Continual Learning for Multilingual Machine Translation via
Vocabulary Substitution [16.939016405962526]
We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models.
Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts and incurs only minor degradation on the translation performance for the original language pairs.
arXiv Detail & Related papers (2021-03-11T17:10:21Z) - XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages.
We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
arXiv Detail & Related papers (2020-05-01T12:22:33Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z) - Knowledge Distillation for Multilingual Unsupervised Neural Machine
Translation [61.88012735215636]
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
UNMT can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time.
In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder.
arXiv Detail & Related papers (2020-04-21T17:26:16Z)
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.