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Found 7 papers, 3 papers with code
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations
Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis.
Ranked #14 on
Word Sense Disambiguation
on Supervised:
Upping the Ante: Towards a Better Benchmark for Chinese-to-English Machine Translation
Our goal in this paper is to propose a benchmark in evaluation setup for Chinese-to-English machine translation, such that the effectiveness of a new proposed MT approach can be directly compared to previous approaches.
A Dependency-Based Neural Reordering Model for Statistical Machine Translation
In machine translation (MT) that involves translating between two languages with significant differences in word order, determining the correct word order of translated words is a major challenge.
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Reordering poses a major challenge in machine translation (MT) between two languages with significant differences in word order.
