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Found 15 papers, 2 papers with code
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist
We conduct experiments for evaluating an offensive content detection system and use a data augmentation technique for improving the model using insights from Checklist.
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer
Few-shot transfer often shows substantial gain over zero-shot transfer (CITATION), which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretained model-based systems.
DiTTO: A Feature Representation Imitation Approach for Improving Cross-Lingual Transfer
Zero-shot cross-lingual transfer is promising, however has been shown to be sub-optimal, with inferior transfer performance across low-resource languages.
Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models
Leveraging shared learning through Massively Multilingual Models, state-of-the-art machine translation models are often able to adapt to the paucity of data for low-resource languages.
On the Calibration of Massively Multilingual Language Models
Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer.
"Diversity and Uncertainty in Moderation" are the Key to Data Selection for Multilingual Few-shot Transfer
Few-shot transfer often shows substantial gain over zero-shot transfer~\cite{lauscher2020zero}, which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretrained model-based systems.
On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data
Borrowing ideas from {\em Production functions} in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models.
Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models
Massively Multilingual Transformer based Language Models have been observed to be surprisingly effective on zero-shot transfer across languages, though the performance varies from language to language depending on the pivot language(s) used for fine-tuning.
Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages
Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity.
Multilingual CheckList: Generation and Evaluation
We compare the TEA CheckLists with CheckLists created with different levels of human intervention.
Predicting the Performance of Multilingual NLP Models
Recent advancements in NLP have given us models like mBERT and XLMR that can serve over 100 languages.
On the Universality of Deep Contextual Language Models
Deep Contextual Language Models (LMs) like ELMO, BERT, and their successors dominate the landscape of Natural Language Processing due to their ability to scale across multiple tasks rapidly by pre-training a single model, followed by task-specific fine-tuning.
GLUECoS : An Evaluation Benchmark for Code-Switched NLP
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
A New Dataset for Natural Language Inference from Code-mixed Conversations
Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the world.
Translating Web Search Queries into Natural Language Questions
For example, for the user entered query "capital of USA", the most probable question intent is "What's the capital of USA?".
