Papers with embedding-based approach
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
A Discriminative Latent-Variable Model for Bilingual Lexicon Induction (D18-1)
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| Challenge: | Existing methods for bilingual lexicon induction take advantage of word embeddings, but our model is not as efficient as previous work. |
| Approach: | They propose a discriminative latent-variable model for bilingual lexicon induction that combines the bipartite matching dictionary prior and an embedding-based approach. |
| Outcome: | The proposed model outperforms existing models on six language pairs and shows that it mitigates hubness problem. |
Neural network embeddings recover value dimensions from psychometric survey items on par with human data (2026.findings-eacl)
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| Challenge: | Embedings from large language models can recover structure of human values . quantitative analysis reveals that SQuID addresses the challenge of obtaining negative correlations between dimensions without domain-specific fine-tuning or training data reannotation. |
| Approach: | They propose to use questionnaire item embeddings to recover human values from PVQ-RR . their results have implications for psychometrics and social science research . |
| Outcome: | The proposed method explains 55% variance in dimension-dimension similarities compared to human data. |
Automatic Evaluation of Language Generation Technology Based on Structure Alignment (2025.coling-main)
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| Challenge: | Existing methods for automatic evaluation ignore syntax of sentences despite its importance in determining meaning. |
| Approach: | They propose an automatic evaluation metric that considers both the words in sentences and their syntactic structures. |
| Outcome: | The proposed method is comparable to baselines from two NLP tasks. |
E-Gen: Leveraging E-Graphs to Improve Continuous Representations of Symbolic Expressions (2025.naacl-long)
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| Challenge: | Existing methods for embedding mathematical expressions are limited by the size and diversity of training data. |
| Approach: | They propose an e-graph-based dataset generation scheme that synthesizes large and diverse datasets. |
| Outcome: | The proposed method outperforms state-of-the-art large language models on several tasks. |
Predicting Human Translation Difficulty Using Automatic Word Alignment (2023.findings-acl)
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| Challenge: | Translation difficulty is a problem when translators are required to resolve translation ambiguity from multiple possible translations. |
| Approach: | They use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. |
| Outcome: | The proposed method improves on a previous embedding-based approach and can contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty. |