Challenge: Using development sets for low-resource training is often more effective . however, some studies show that early stopping can overestimate performance .
Approach: They find that early stopping on a development set is more effective than using all available data for training.
Outcome: The proposed model overestimates accuracy over languages and tasks by 1.4% compared to a more realistic set of training epochs.

Similar Papers

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)

Copied to clipboard

Challenge: a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements .
Approach: They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting.
Outcome: The proposed methods enable learning when training data is sparse.
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models (2024.findings-eacl)

Copied to clipboard

Challenge: Pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages by substantial margins, as measured by both automatic and human evaluations.
Approach: They propose to use pretrained large language models to bridge this gap by automating and evaluating data-to-text generation in under-resourced languages.
Outcome: The proposed model can set the state of the art for under-resourced languages by substantial margins, as measured by both automatic and human evaluations.
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)

Copied to clipboard

Challenge: Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings.
Approach: They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data.
Outcome: The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data.
Efficient Methods for Natural Language Processing: A Survey (2023.tacl-1)

Copied to clipboard

Challenge: Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data, but using only scale to improve performance means resource consumption also grows.
Approach: They propose to use data, time, storage, or energy to improve model performance.
Outcome: The proposed methods and findings provide guidance for conducting NLP under limited resources and point towards promising research directions for developing more efficient methods.
Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (2022.findings-emnlp)

Copied to clipboard

Challenge: Existing studies have examined the quality of labeled data in non-English languages.
Approach: They annotate how datasets are created, input text and label sources, tools used to build them and what they study.
Outcome: The results show that language-proficient NLP researchers' estimated availability correlates with dataset availability.
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)

Copied to clipboard

Challenge: Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs.
Approach: They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production.
Outcome: The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise.
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)

Copied to clipboard

Challenge: Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting .
Approach: They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models .
Outcome: The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects.
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023.eacl-main)

Copied to clipboard

Challenge: Several strategies have been proposed to enhance performance in low-resource scenarios.
Approach: They propose to use 5 low-resource strategies for dependency parsing for multiple languages . they use ensembled approach on 7 UD low-rsource languages based on their results .
Outcome: The proposed approach improves on a low-resource language Sanskrit.
A Natural Bias for Language Generation Models (2023.acl-short)

Copied to clipboard

Challenge: a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, making it difficult to estimate the probability distribution over next tokens.
Approach: They propose to initialise bias terms in a model's final linear layer with the log-unigram distribution and use it to output the unigram frequency statistics as prior knowledge.
Outcome: The proposed method improves learning efficiency and improves overall performance.
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)

Copied to clipboard

Challenge: Natural language processing (NLP) is a vast field, with a wide variety of tasks, languages, and domains.
Approach: They build regression models to predict evaluation score of an NLP experiment . they find that their models can produce meaningful predictions over unseen languages .
Outcome: The proposed model outperforms baseline models and human experts on 9 different tasks.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations