English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Natural language understanding(NLU) is challenging for finance due to thelack of annotated data and the specialized language in that domain. As aresult, researchers have proposed to use pre-trained language model andmulti-task learning to learn robust representations. However, aggressivefine-tuning often causes over-fitting and multi-task learning may favor taskswith significantly larger amounts data, etc. To address these problems, in thispaper, we investigate model-agnostic meta-learning algorithm(MAML) inlow-resource financial NLU tasks. Our contribution includes: 1. we explore theperformance of MAML method with multiple types of tasks: GLUE datasets, SNLI,Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML methodwith multiple single-type tasks: a real scenario stock price prediction problemwith twitter text data. Our models achieve the state-of-the-art performanceaccording to the experimental results, which demonstrate that our method canadapt fast and well to low-resource situations.

Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance