Papers with sentence encoder
Learning Visually Grounded Sentence Representations (N18-1)
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
| Outcome: | The proposed model improves on word embeddings and word representations on standard benchmarks. |
Discrete Cosine Transform as Universal Sentence Encoder (2021.acl-short)
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| Challenge: | Modern sentence encoders capture underlying linguistic characteristics of words . Discrete Cosine Transform (DCT) is an efficient alternative to averaging . |
| Approach: | They propose to use a Discrete Cosine Transform to generate universal sentence representations in different languages. |
| Outcome: | The proposed model captures the underlying syntactic characteristics of a given text without compromising practical efficiency. |
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation (D19-1)
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| Challenge: | Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context. |
| Approach: | They propose a hierarchical model to learn document context for document-level neural machine translation . they use a sentence encoder to capture intra-sentence dependencies and a document encoder . |
| Outcome: | The proposed model significantly improves document-level translation performance over strong baselines. |
Hierarchical User and Item Representation with Three-Tier Attention for Recommendation (N19-1)
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| Challenge: | Existing methods to learn user and item representations from reviews are limited . existing methods learn user representations based on ratings given by users . |
| Approach: | They propose a hierarchical user and item representation model with three-tier attention to learn user and items from reviews for recommendation. |
| Outcome: | The proposed model can learn user and item representations from reviews on four benchmark datasets. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |
Continual Learning for Sentence Representations Using Conceptors (N19-1)
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| Challenge: | Existing sentence encoders for distributed representations of sentences are limited in their performance on fixed corpora. |
| Approach: | They propose a continual learning scenario for distributed representations of sentences . they initialize sentence encoders with corpus-independent features and update them sequentially . |
| Outcome: | The proposed sentence encoder can learn features from new corpora while maintaining its competence on previously encountered corporales. |
Intermediate Self-supervised Learning for Machine Translation Quality Estimation (2020.coling-main)
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| Challenge: | Existing methods for machine translation quality estimation (QE) rely on annotated data. |
| Approach: | They propose a self-supervised learning task for machine translation (MT) that orients a pre-trained model towards the target task. |
| Outcome: | The proposed method outperforms existing methods on English-to-German and English- to-Russian translation directions and is comparable to existing models. |
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)
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| Challenge: | Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process . |
| Approach: | They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders . |
| Outcome: | The proposed approach can be used to make an effective decision-making tool for the academic paper review process. |
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses . |
| Approach: | They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space. |
| Outcome: | The proposed model generalizes over seen and unseen senses, achieving zero-shot learning. |
Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy (2026.acl-long)
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| Challenge: | a method to compare and visualise sentence encoders at scale is proposed . we map encoder LLMs using QRE-based feature vectors, which are then projected to 2D . |
| Approach: | They propose a method to compare and visualise sentence encoders at scale by creating a map of encoder . they construct a QRE-based map of sentences covering 1101 publicly available sentence encoded sentences . |
| Outcome: | The proposed method compares sentence encoders at scale by creating a map of encoder models . it shows that the map accurately reflects relationships between encoder and unit base encoder . |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders (2021.emnlp-main)
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| Challenge: | Existing studies have used probing tasks to verify the presence of linguistic properties in vector representations, but it is unclear whether they can be manipulated to indirectly steer them. |
| Approach: | They validate a geometric mapping technique to transform linguistic properties without tuning . they use a pre-trained multilingual autoencoder to transform three linguistic property . |
| Outcome: | The proposed method can be used without tuning of the pre-trained autoencoder . the results are validated in monolingual and cross-lingual settings . |