Event-Guided Denoising for Multilingual Relation Learning
| Autor: | Kathleen R. McKeown, Amith Ananthram, Emily Allaway |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Relation (database) Process (engineering) business.industry Computer science Event (computing) media_common.quotation_subject 010102 general mathematics computer.software_genre 01 natural sciences Relationship extraction Quality (business) Fraction (mathematics) Artificial intelligence 0101 mathematics business computer Natural language processing media_common |
| Zdroj: | COLING |
| DOI: | 10.18653/v1/2020.coling-main.131 |
| Popis: | General purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many benchmarks. In this work, we present a methodology for collecting high quality training data for relation extraction from unlabeled text that achieves a near-recreation of their zero-shot and few-shot results at a fraction of the training cost. Our approach exploits the predictable distributional structure of date-marked news articles to build a denoised corpus – the extraction process filters out low quality examples. We show that a smaller multilingual encoder trained on this corpus performs comparably to the current state-of-the-art (when both receive little to no fine-tuning) on few-shot and standard relation benchmarks in English and Spanish despite using many fewer examples (50k vs. 300mil+). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|