Melvin Johnson
Melvin Johnson joined Google in 2015 where he works on Machine Translation and Natural Language Processing.
Before Google, Melvin obtained a Masters degree in Computer Science from Stanford University where he worked with Prof. Chris Manning.
Before Google, Melvin obtained a Masters degree in Computer Science from Stanford University where he worked with Prof. Chris Manning.
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XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder
Mihir Sanjay Kale
Shruti Rijhwani
Jean-Michel Sarr
Cindy Wang
John Wieting
Christo Kirov
Dana L. Dickinson
Bidisha Samanta
Connie Tao
David Adelani
Reeve Ingle
Dmitry Panteleev
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 1856-1884
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Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.
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We present Mu2SLAM, a multilingual sequence-to-sequence model pre-trained jointly on un-labeled 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, Mu2SLAM trains ona sequence-to-sequence masked denoising objective similar to T5 on both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoSTAST, Mu2SLAM establishes a new state-of-the-art for models trained on public datasets, improv-ing on xx-en translation over the previous best by 1.9 Bleu points and on en-xx translation by 0.9 Bleu points. On Voxpopuli ASR, our model matches the performance of a mSLAM model finetuned with a 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.
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XTREME-S: Evaluating Cross-lingual Speech Representations
Clara E. Rivera
Mihir Sanjay Kale
Sebastian Ruder
Simran Khanuja
Ye Jia
Yu Zhang
Proc. Interspeech 2022
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We introduce \xtremes, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classification, retrieval and speech-to-text translation. Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in ``universal'' speech representation learning. This paper describes the new benchmark and establishes the first speech-only and speech-text baselines using XLS-R and mSLAM on all downstream tasks. We motivate the design choices and detail how to use the benchmark. The code and pre-processing scripts will be made publicly available.\footnote{\small\url{https://huggingface.co/datasets/google/xtreme_s}}
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Research in natural language processing that focuses solely on binary genders can pose the serious danger of excluding communities and behaviors that are gender nonconforming. In this paper, we highlight the use of gender-inclusive language by proposing the task of rewriting gendered sentences in English to be gender-neutral using the \textit{singular they}. To this end, we train a Seq2Seq model for this task by creating a rewriting algorithm to generate a parallel dataset and evaluate performance on an annotated test set of 500 sentence-pairs (gendered to gender-neutral). Impressively, we are able to achieve over 99 BLEU and less than 1\% word error rate for both the algorithm and the model. Finally, we give some practical applications for this task, including machine translation and augmented writing.
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MergeDistill: Merging Pre-trained Language Models using Distillation
Simran Khanuja
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
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Pre-trained multilingual language models (LMs) have achieved state-of-the-art results in cross-lingual transfer, but they often lead to an inequitable representation of languages due to limited capacity, skewed pre-training data, and sub-optimal vocabularies. This has prompted the creation of an ever-growing pre-trained model universe, where each model is trained on large amounts of language or domain specific data with a carefully curated, linguistically informed vocabulary. However, doing so brings us back full circle and prevents one from leveraging the benefits of multilinguality. To address the gaps at both ends of the spectrum, we propose MergeDistill, a framework to merge pre-trained LMs in a way that can best leverage their assets with minimal dependencies, using task-agnostic knowledge distillation. We demonstrate the applicability of our framework in a practical setting by leveraging pre-existing teacher LMs and training student LMs that perform competitively with or even outperform teacher LMs trained on several orders of magnitude more data and with a fixed model capacity. We also highlight the importance of teacher selection and its impact on student model performance.
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nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?
Linting Xue
Mihir Sanjay Kale
Rami Al-Rfou
Annual Meeting of the Association for Computational Linguistics (ACL) (2021) (to appear)
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Recently, mT5 - a massively multilingual version of T5 - leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks. In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training. We find that simply multi-tasking language modeling with objectives such as machine translation during pre-training leads to improved performance on downstream multilingual and cross-lingual tasks. However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models. At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime.
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Back-translation (BT) of target monolingual corpora is a widely used data augmentation strategy for neural machine translation (NMT), especially for low-resource language pairs. To improve the effectiveness of the available BT data, we introduce HintedBT -- a family of techniques which provides hints (through tags) to the encoder and decoder. First, we propose a novel method of using \textit{both high and low quality} BT data by providing hints (as encoder tags) to the model about the quality of each source-target pair. We don't filter out low quality data but instead show that these hints enable the model to learn effectively from noisy data.
Second, we address the problem of predicting whether a source token needs to be translated or transliterated to the target language, which is common in cross-script translation tasks (i.e., where source and target do not share the written script).
For such cases, we propose training the model with additional hints (as decoder tags) that provide information about the \textit{operation} required on the source (translation or both translation and transliteration). We conduct experiments and detailed analyses on standard WMT benchmarks for three cross-script low/medium-resource language pairs: \{Hindi,Gujarati,Tamil\}$\rightarrow$English.
Our methods compare favorably with five strong and well established baselines. We show that using these hints, both separately and together, significantly improves translation quality and leads to state-of-the-art performance in all three language pairs in corresponding bilingual settings.
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Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation
Henry Tsai
Naveen Ari
AAAI 2020 (2020)
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Recently proposed Massively Multilingual Neural Machine Translation system has been shown to be capable of translating 102 languages to and from English within a single model. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of such a model on 5 downstream classification and sequence tagging tasks spanning more than 50 languages. We compare our results to a strong multilingual baseline, BERT and show modest gains on zero-shot cross-lingual transfer in 4 out of these 5 tasks. Our results provide strong insight into how applicable the representations learned from multilingual machine translation are, across languages and tasks.
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Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing.
To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We will release the benchmark to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.
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Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation
Ye Jia
Chung-Cheng Chiu
Naveen Ari
Stella Marie Laurenzo
ICASSP (2019)
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End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models, including
lowered inference latency and the avoidance of error compounding.
However, the quality of end-to-end ST is often limited by a paucity
of training data, since it is difficult to collect large parallel corpora of
speech and translated transcript pairs. Previous studies have proposed
the use of pre-trained components and multi-task learning in order
to benefit from weakly supervised training data, such as speech-totranscript or text-to-foreign-text pairs. In this paper, we demonstrate
that using pre-trained MT or text-to-speech (TTS) synthesis models
to convert weakly supervised data into speech-to-translation pairs for
ST training can be more effective than multi-task learning. Furthermore, we demonstrate that a high quality end-to-end ST model can
be trained using only weakly supervised datasets, and that synthetic
data sourced from unlabeled monolingual text or speech can be used
to improve performance. Finally, we discuss methods for avoiding
overfitting to synthetic speech with a quantitative ablation study.
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