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. |
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Sentence Smith: Controllable Edits for Evaluating Text Embeddings (2025.emnlp-main)
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| Challenge: | Controllable and transparent text generation has been a long-standing goal in NLP . but previous approaches were hindered by parsing and generation insufficiencies . |
| Approach: | They propose a framework for English that has three steps: 1. Parsing a sentence into a semantic graph. 2. Applying human-designed semantic manipulation rules. 3. Generating text from the manipulated graph. |
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Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning (2022.acl-long)
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| Challenge: | Pre-trained sequence-to-sequence language models generate structured outputs such as graphs with limited supervision. |
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Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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MGAD: Multilingual Generation of Analogy Datasets (L18-1)
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| Challenge: | Existing methods for word embedding evaluation are computationally expensive and task-specific. |
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Improving Text Embeddings with Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
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GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation (2022.coling-1)
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| Challenge: | Recent improvements in KG-to-text generation are due to additional pre-training tasks . these tasks require extensive computational resources while only suggesting marginal improvements. |
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Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
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Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry (2025.emnlp-industry)
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| Challenge: | Recent trends in NLP utilize knowledge graphs to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific terminology or relationships between documents that might otherwise be overlooked. |
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Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
| Approach: | They propose a method for improving BERT's performance by using a label embedding technique while keeping almost the same computational cost. |
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