Papers with sentence pair modeling tasks
Character-Based Neural Networks for Sentence Pair Modeling (N18-2)
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| Challenge: | Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification and semantic textual similarity. |
| Approach: | They propose to use subwords to represent sentences without pretrained word embeddings . they find that subword models can achieve new state-of-the-art results without pretraining . |
| Outcome: | The proposed models can achieve state-of-the-art results on two social media datasets and competitive results on news data for paraphrase identification. |
Paraphrase-based Contrastive Learning for Sentence Pair Modeling (2025.naacl-srw)
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| Challenge: | Existing methods to improve performance of sentence pair modeling are not available on a large-scale for non-English languages. |
| Approach: | They propose a method to apply contrastive learning to pre-trained masked language models . they use sentence embeddings of paraphrase pairs to make similar sentences . |
| Outcome: | The proposed method can be used on four sentence pair modeling tasks in English and Japanese. |
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling (2023.emnlp-main)
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| Challenge: | Recent studies suggest that transformer-based models perform cross-attention over input pairs, leading to computational cost. |
| Approach: | They propose a lightweight cross-attention mechanism that performs query encoding only once while modeling the query-candidate interaction in parallel. |
| Outcome: | The proposed model speeds up sentence pairing by over 113x while achieving comparable performance as the more expensive models. |