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Found 18 papers, 4 papers with code
An image speaks a thousand words, but can everyone listen? On translating images for cultural relevance
First, we build three pipelines comprising state-of-the-art generative models to do the task.
What Is Missing in Multilingual Visual Reasoning and How to Fix It
NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users.
DeMuX: Data-efficient Multilingual Learning
In this paper, we introduce DEMUX, a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set.
Multi-lingual and Multi-cultural Figurative Language Understanding
Figurative language permeates human communication, but at the same time is relatively understudied in NLP.
GlobalBench: A Benchmark for Global Progress in Natural Language Processing
Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist.
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages
In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world's languages, be equitable, i. e., not unduly biased towards any particular language, and be inclusive of all users, particularly in low-resource settings where compute constraints are common.
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.
mSLAM: Massively multilingual joint pre-training for speech and text
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple languages.
MergeDistill: Merging Pre-trained Language Models using Distillation
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.
MuRIL: Multilingual Representations for Indian Languages
This can be explained by the fact that multilingual language models (LMs) are often trained on 100+ languages together, leading to a small representation of IN languages in their vocabulary and training data.
Cross-lingual and Multilingual Spoken Term Detection for Low-Resource Indian Languages
Since no publicly available dataset exists for Spoken Term Detection in these languages, we create a new dataset using a publicly available TTS dataset.
GLUECoS: An Evaluation Benchmark for Code-Switched NLP
We present results on all these tasks using cross-lingual word embedding models and multilingual models.
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.
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities.
