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Found 19 papers, 9 papers with code
Multitask Multilingual Model Adaptation with Featurized Low-Rank Mixtures
By composing feature-specific parameters for each dataset, FLix can accommodate diverse dataset mixtures and generalize better to unseen datasets.
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning.
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems.
Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Based on these models, we improve the attribution level of a cross-lingual question-answering system.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
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AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages
African languages have far less in-language content available digitally, making it challenging for question answering systems to satisfy the information needs of users.
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval
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.
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages.
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
XTREME-S: Evaluating Cross-lingual Speech Representations
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.
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech.
CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.
CapWAP: Captioning with a Purpose
In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation.
Learning to Recognize Dialect Features
Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples.
XOR QA: Cross-lingual Open-Retrieval Question Answering
Multilingual question answering tasks typically assume answers exist in the same language as the question.
TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages
Confidently making progress on multilingual modeling requires challenging, trustworthy evaluations.
Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization
Linear models, which support efficient learning and inference, are the workhorses of statistical machine translation; however, linear decision rules are less attractive from a modeling perspective.
