We present a new approach to perform zero-shot cross-modal transfer between speech and text for translation tasks. Multilingual speech and text are encoded in a joint fixed-size representation space. Then, we compare different approaches to decode these multimodal and multilingual fixed-size representations, enabling zero-shot translation between languages and modalities. All our models are trained without the need of cross-modal labeled translation data. Despite a fixed-size representation, we achieve very competitive results on several text and speech translation tasks. In particular, we significantly improve the state-of-the-art for zero-shot speech translation on Must-C. Incorporating a speech decoder in our framework, we introduce the first results for zero-shot direct speech-to-speech and text-to-speech translation.

We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available.

End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the evaluation dependent on the availability and quality of automatic speech recognition (ASR) systems. In this paper, we propose a text-free evaluation metric for end-to-end S2ST, named BLASER, to avoid the dependency on ASR systems. BLASER leverages a multilingual multimodal encoder to directly encode the speech segments for source input, translation output and reference into a shared embedding space and computes a score of the translation quality that can be used as a proxy to human evaluation. To evaluate our approach, we construct training and evaluation sets from more than 40k human annotations covering seven language directions. The best results of BLASER are achieved by training with supervision from human rating scores. We show that when evaluated at the sentence level, BLASER correlates significantly better with human judgment compared to ASR-dependent metrics including ASR-SENTBLEU in all translation directions and ASR-COMET in five of them. Our analysis shows combining speech and text as inputs to BLASER does not increase the correlation with human scores, but best correlations are achieved when using speech, which motivates the goal of our research. Moreover, we show that using ASR for references is detrimental for text-based metrics.

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes an encoder, decoder and attention module, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-the-art results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT'14 and WMT'15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and show some interesting examples when mixing languages.

以前的工作主要侧重于改善NLU任务的交叉传输，具有多语言预用编码器（MPE），或提高与伯特的监督机器翻译的性能。然而，探索了，MPE是否可以有助于促进NMT模型的交叉传递性。在本文中，我们专注于NMT中的零射频转移任务。在此任务中，NMT模型培训，只有一个语言对的并行数据集和搁置架MPE，然后它直接测试在零拍语言对上。我们为此任务提出了Sixt，一个简单而有效的模型。 SIXT利用了两阶段培训计划利用MPE，并进一步改进了解离编码器和容量增强的解码器。使用此方法，SIMPT显着优于MBart，这是一个用于NMT的预磨削的多语言编码器解码器模型，平均改善了14个源语言的零拍摄的任何英语测试集上的7.1 BLEU。此外，培训计算成本和培训数据较少，我们的模型在15个任何英语测试组上实现了比Criss和M2M-100，两个强大的多语言NMT基线更好的性能。

State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single language (usually English), and cannot be directly used beyond that language. Since collecting data in every language is not realistic, there has been a growing interest in crosslingual language understanding (XLU) and low-resource cross-language transfer. In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu. We hope that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence understanding by providing an informative standard evaluation task. In addition, we provide several baselines for multilingual sentence understanding, including two based on machine translation systems, and two that use parallel data to train aligned multilingual bag-of-words and LSTM encoders. We find that XNLI represents a practical and challenging evaluation suite, and that directly translating the test data yields the best performance among available baselines.

我们提出了一种两阶段的培训方法，用于开发单个NMT模型，以翻译英语和英语的看不见的语言。对于第一阶段，我们将编码器模型初始化以鉴定XLM-R和Roberta的权重，然后对25种语言的平行数据进行多种语言微调。我们发现该模型可以推广到对看不见的语言的零击翻译。在第二阶段，我们利用这种概括能力从单语数据集生成合成的并行数据，然后用连续的反向翻译训练。最终模型扩展到了英语到许多方向，同时保持了多到英语的性能。我们称我们的方法为ecxtra（以英语为中心的跨语言（x）转移）。我们的方法依次利用辅助并行数据和单语言数据，并且在概念上很简单，仅在两个阶段都使用标准的跨熵目标。最终的ECXTRA模型对8种低资源语言的无监督NMT进行了评估，该语言为英语至哈萨克语（22.3> 10.4 bleu）以及其他15个翻译方向的竞争性能而获得了新的最先进。

End-to-end Speech Translation (E2E ST) aims to translate source speech into target translation without generating the intermediate transcript. However, existing approaches for E2E ST degrade considerably when only limited ST data are available. We observe that an ST model's performance strongly correlates with its embedding similarity from speech and transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a novel method for few-shot speech-to-text translation. Our key idea is bridging word-level representations for both modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark. Our experiments demonstrate that WACO outperforms the best baseline methods by 0.7-8.5 BLEU points with only 1-hour parallel data. Code is available at https://anonymous.4open.science/r/WACO .

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data. In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS. The performance can be further enhanced when cross-lingual NLI data is available. Our code is publicly available at https://github.com/yaushian/mSimCSE.

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective . mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.

字幕（替代）的语音翻译是通过将符合特定显示指南的字幕划分插入字幕中断，将语音数据自动转化为良好的字幕。与语音翻译（ST）类似，模型训练需要并行数据，其中包括音频输入与其文本翻译配对。然而，在替代方面，还必须用字幕断裂来注释文本。到目前为止，这一要求代表了系统开发的瓶颈，如公开可用的替代公司所证实。为了填补这一空白，我们提出了一种在不干预的情况下将现有的ST Corpora转换为替代资源的方法。我们构建了一个分段模型，该模型通过以多模式的方式利用音频和文本来自动将文本片段分为适当的字幕，从而在零拍摄条件下实现了高分子的质量。对手动和自动分割培训的替代系统的比较实验导致相似的性能，显示了我们方法的有效性。

We present Mu$^{2}$SLAM, a multilingual sequence-to-sequence model pre-trained jointly on unlabeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition (ASR), Automatic Speech Translation (AST) and Machine Translation (MT), in over 100 languages. By leveraging a quantized representation of speech as a target, Mu$^{2}$SLAM trains the speech-text models with a sequence-to-sequence masked denoising objective similar to T5 on the decoder and a masked language modeling (MLM) objective on the encoder, for both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoST AST, Mu$^{2}$SLAM establishes a new state-of-the-art for models trained on public datasets, improving on xx-en translation over the previous best by 1.9 BLEU points and on en-xx translation by 1.1 BLEU points. On Voxpopuli ASR, our model matches the performance of an mSLAM model fine-tuned with an RNN-T decoder, despite using a relatively weaker sequence-to-sequence architecture. On text understanding tasks, our model improves by more than 6\% over mSLAM on XNLI, getting closer to the performance of mT5 models of comparable capacity on XNLI and TydiQA, paving the way towards a single model for all speech and text understanding tasks.

Direct speech-to-speech translation (S2ST), in which all components can be optimized jointly, is advantageous over cascaded approaches to achieve fast inference with a simplified pipeline. We present a novel two-pass direct S2ST architecture, {\textit UnitY}, which first generates textual representations and predicts discrete acoustic units subsequently. We enhance the model performance by subword prediction in the first-pass decoder, advanced two-pass decoder architecture design and search strategy, and better training regularization. To leverage large amounts of unlabeled text data, we pre-train the first-pass text decoder based on the self-supervised denoising auto-encoding task. Experimental evaluations on benchmark datasets at various data scales demonstrate that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. We show that the proposed methods boost the performance even when predicting spectrogram in the second pass. However, predicting discrete units achieves 2.51x decoding speed-up compared to that case.

本文介绍了基于Wav2VEC 2.0的跨语言语音表示学习的大规模模型。我们在128种语言中培训最多2B个公共讲话音频的近半小时的型号的模型，比公共数据的数量级比最大的已知事先工作。我们的评估涵盖了广泛的任务，域，数据制度和语言，都是高低资源。在Covost-2语音翻译基准测试中，我们将先前的最先进的状态平均为7.4 BLEU超过21个翻译方向进入英语。对于语音识别，XLS-R在Babel，MLS，CommonVoice以及Voxpopuli上的最佳已知工作中提高，降低了相对的误差率14-34％。 XLS-R还在Voxlingua107语言识别上设置了新的技术状态。此外，我们表明，具有足够的模型规模，交叉思维预先预测可以在将英语演讲翻译成其他语言时才能优于英语撇印，这是一个有利于单晶的预借预制的设置。我们希望XLS-R可以帮助改善世界上更多语言的语音处理任务。

在这项工作中，我们提出了一个系统的实证研究，专注于最先进的多语言编码器在跨越多种不同语言对的交叉语言文档和句子检索任务的适用性。我们首先将这些模型视为多语言文本编码器，并在无监督的ad-hoc句子和文档级CLIR中基准性能。与监督语言理解相比，我们的结果表明，对于无监督的文档级CLIR - 一个没有针对IR特定的微调 - 预训练的多语言编码器的相关性判断，平均未能基于CLWE显着优于早期模型。对于句子级检索，我们确实获得了最先进的性能：然而，通过多语言编码器来满足高峰分数，这些编码器已经进一步专注于监督的时尚，以便句子理解任务，而不是使用他们的香草'现货'变体。在这些结果之后，我们介绍了文档级CLIR的本地化相关性匹配，在那里我们独立地对文件部分进行了查询。在第二部分中，我们评估了在一系列零拍语言和域转移CLIR实验中的英语相关数据中进行微调的微调编码器精细调整的微调我们的结果表明，监督重新排名很少提高多语言变压器作为无监督的基数。最后，只有在域名对比度微调（即，同一域名，只有语言转移），我们设法提高排名质量。我们在目标语言中单次检索的交叉定向检索结果和结果（零拍摄）交叉传输之间的显着实证差异，这指出了在单机数据上训练的检索模型的“单声道过度装备”。

多语种NMT已成为MT在生产中部署的有吸引力的解决方案。但是要匹配双语质量，它符合较大且较慢的型号。在这项工作中，我们考虑了几种方法在推理时更快地使多语言NMT变得更快而不会降低其质量。我们在两种20语言多平行设置中尝试几个“光解码器”架构：在TED会谈中小规模和帕拉克曲线上的大规模。我们的实验表明，将具有词汇过滤的浅解码器组合在于，在翻译质量下没有损失的速度超过两倍。我们用Bleu和Chrf（380语言对），鲁棒性评估和人类评估验证了我们的研究结果。

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. In other words, the multilingual multimodal machine translation (Multilingual MMT) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for multiple languages. Besides, the image modality has no language boundaries, which is superior to bridging the semantic gap between languages. To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages. Then, an effective baseline LVP-M3 using visual prompts is proposed to support translations between different languages, which includes three stages (token encoding, language-aware visual prompt generation, and language translation). Extensive experimental results on our constructed benchmark datasets demonstrate the effectiveness of LVP-M3 method for Multilingual MMT.

多语言语言模型（\ mllms），如mbert，xlm，xlm-r，\ textit {etc。}已成为一种可行的选择，使预先估计到大量语言的力量。鉴于他们的成功在零射击转移学习中，在（i）建立更大的\ mllms〜覆盖了大量语言（ii）创建覆盖更广泛的任务和语言来评估的详尽工作基准mllms〜（iii）分析单音零点，零拍摄交叉和双语任务（iv）对Monolingual的性能，了解\ mllms〜（v）增强（通常）学习的通用语言模式（如果有的话）有限的容量\ mllms〜以提高他们在已见甚至看不见语言的表现。在这项调查中，我们审查了现有的文学，涵盖了上述与\ MLLMS有关的广泛研究领域。根据我们的调查，我们建议您有一些未来的研究方向。

多语言神经机器翻译（MNMT）使一个系统能够将句子从多种源语言转换为多种目标语言，与传统的双语系统相比，大大降低了部署成本。但是，MNMT培训益处通常仅限于多一对一的方向。该模型在一对一的表现不佳，并且在零镜头设置中遭受了多种影响。为了解决这个问题，本文讨论了如何实际构建提供任意X-Y翻译指示的MNMT系统，同时使用预处理和填充的两阶段培训策略利用多语言。尝试WMT'21多语言翻译任务，我们证明我们的系统的表现优于大多数方向的直接双语模型和枢轴翻译模型的传统基线，平均提供+6.0和+4.1 BLEU，而无需进行架构更改或额外的数据收集。 。此外，我们还在极大的数据设置中检查了我们提出的方法，以适应实际的部署方案。

Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in $N$ languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval -- the last of which we introduce in this paper. Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks.