Representing ELMo embeddings as two-dimensional text online
Not on the map. Why?
Abstract (arXiv)
We describe a new addition to the WebVectors toolkit which is used to serve word embedding models over the Web. The new ELMoViz module adds support for contextualized embedding architectures, in particular for ELMo models. The provided visualizations follow the metaphor of `two-dimensional text' by showing lexical substitutes: words which are most semantically similar in context to the words of the input sentence. The system allows the user to change the ELMo layers from which token embeddings are inferred. It also conveys corpus information about the query words and their lexical substitutes (namely their frequency tiers and parts of speech). The module is well integrated into the rest of the WebVectors toolkit, providing lexical hyperlinks to word representations in static embedding models. Two web services have already implemented the new functionality with pre-trained ELMo models for Russian, Norwegian and English.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 30 Mar 2021.
Builds on
8 of 18 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2018Deep contextualized word representationscited by 2,290
- 2016Enriching Word Vectors with Subword Informationcited by 1,583
- 2018Dissecting Contextual Word Embeddings: Architecture and Representationcited by 146
- 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
- 2019exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Modelscited by 33
- 2020UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detectioncited by 32
Built on by
Not cited yet.