Papers with non-linear mapping
Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages (D19-1)
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| Challenge: | Existing methods for mapping monolingual word embeddings into another are based on anchor points and unsupervised methods are more adversarial. |
| Approach: | They propose a noise-tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. |
| Outcome: | The proposed method outperforms the state-of-the-art in lower resourced settings with an average of 3.7% improvement of precision @10 across 14 mostly low resourced languages. |
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space (2020.emnlp-main)
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| Challenge: | Existing methods for bilingual lexicon induction are mapping-based, but they do not hold for closely related languages. |
| Approach: | They propose a semi-supervised method to learn cross-lingual word embeddings for BLI using a linear mapping function and a latent space of two independently trained autoencoders. |
| Outcome: | The proposed method outperforms existing models on 15 different language pairs on both directions. |
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment (2021.findings-acl)
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| Challenge: | Existing models that perform unidirectional mappings are unipolar and unintended . |
| Approach: | They propose a bi-directional mapping algorithm that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. |
| Outcome: | The proposed model reduces the number of models by 50% and improves performance over unidirectional translation models. |
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings (2020.lrec-1)
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| Challenge: | Existing work on cross-lingual word embeddings rely on linear mappings, but this assumption is not true for all language pairs. |
| Approach: | They propose a non-linear mapping approach which can find non-linesar relationships between languages by kernel Canonical Correlation Analysis. |
| Outcome: | The proposed approach improves on five language pairs on supervised and self-learning scenarios. |
Presentations are not always linear! GNN meets LLM for Text Document-to-Presentation Transformation with Attribution (2024.findings-emnlp)
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| Challenge: | Existing approaches to generate presentations from document to slide are difficult to implement and cause hallucination. |
| Approach: | They propose a graph-based solution that uses a combination of graph neural network and LLM to generate a presentation with attribution of content for each slide. |
| Outcome: | The proposed approach is more efficient than using LLMs for generating a presentation from the text of a document. |