Search Results for author:
Found 22 papers, 6 papers with code
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalisation
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.
Ranked #1 on
Zero-Shot Cross-Lingual Transfer
on XTREME
(AVG metric)
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences.
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation
Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task.
Dialect-robust Evaluation of Generated Text
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producing text in lower-resource dialects.
Building Machine Translation Systems for the Next Thousand Languages
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages.
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
While recent progress in massively multilingual MT is one step closer to reaching this goal, it is becoming evident that extending a multilingual MT system simply by training on more parallel data is unscalable, since the availability of labeled data for low-resource and non-English-centric language pairs is forbiddingly limited.
DOCmT5: Document-Level Pretraining of Multilingual Language Models
In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large scale parallel documents.
Ranked #1 on
Document Translation
on IWSLT2015
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?
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.
nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?
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.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation
While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others.
Distilling Large Language Models into Tiny and Effective Students using pQRNN
Our strong results suggest that our approach is great for latency-sensitive applications while being able to leverage large mBERT-like models.
mT5: A massively multilingual pre-trained text-to-text transformer
The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks.
Ranked #2 on
Reading Comprehension
on MuSeRC
Explicit Alignment Objectives for Multilingual Bidirectional Encoders
Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages.
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages
We outperform all current state-of-the-art unsupervised baselines for these languages, achieving gains of up to 14. 4 BLEU.
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation
Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged.
XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
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.
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation
The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model.
Domain Adaptation of SRL Systems for Biological Processes
Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems.
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks
Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer.
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study
This paper provides a large scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions.
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems.
