Papers with retrofitting
Explicit Retrofitting of Distributional Word Vectors (P18-1)
Copied to clipboard
| Challenge: | Existing models for word vector specialization focus on word co-occurrences from large text corpora, resulting in a tendency to fuse semantic similarity with other types of semantic relatedness. |
| Approach: | They propose to transform external lexico-semantic relations into training examples and learn an explicit retrofitting model. |
| Outcome: | The proposed model can specialize vector spaces of new languages and translate them to other languages. |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources (N18-1)
Copied to clipboard
| Challenge: | Word vector specialisation is a portable, light-weight approach to fine-tuning distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. |
| Approach: | They propose a constraint-driven vector space specialisation method that embeds external knowledge into lexical resources into a deep neural network to specialise unseen words. |
| Outcome: | The proposed method preserves useful linguistic knowledge for seen words while propagating external signal to unseen words to improve their vector representations. |
What just happened? Evaluating retrofitted distributional word vectors (N19-1)
Copied to clipboard
| Challenge: | Recent work has attempted to enhance vector space representations using information from structured semantic resources. |
| Approach: | They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting. |
| Outcome: | The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric. |
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting (D18-1)
Copied to clipboard
| Challenge: | Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. |
| Approach: | They use a corpus of 16.8M anonymous online posts to learn continuous document representations of cities. |
| Outcome: | The proposed method matches dialect areas at different granularities against an existing dialect map. |