Papers with aggregation mechanism
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
Reproducibility Issues for BERT-based Evaluation Metrics (2022.emnlp-main)
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| Challenge: | Reproducibility is of utmost concern in machine learning and natural language processing . lexical-overlap metrics are still the dominant metric in natural language generation . |
| Approach: | They ask whether results and claims from four recent BERT-based evaluation metrics can be reproduced. |
| Outcome: | The proposed metrics outperform the dominant metric, BLEU, and show that they can be reproduced. |
Information Aggregation via Dynamic Routing for Sequence Encoding (C18-1)
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| Challenge: | Currently, little attention is paid to how to aggregate text sequences into fixed-size vectors. |
| Approach: | They propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. |
| Outcome: | The proposed method outperforms other aggregating methods on five text classification tasks. |
Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have impressive moral reasoning abilities, yet they often diverge when confronted with complex, multi-factor moral dilemmas. |
| Approach: | They propose a framework that synthesizes multiple LLMs’ moral judgments into a collectively formulated moral judgment, realigning models that deviate significantly from this consensus. |
| Outcome: | The proposed framework synthesizes multiple LLMs’ moral judgments into a collectively formulated moral judgment, realigning models that deviate significantly from this consensus. |