Papers with semantic similarity models
A Hybrid Neural Network Model for Commonsense Reasoning (D19-60)
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| Challenge: | a hybrid neural network (HNN) model for commonsense reasoning is proposed . it combines language models and semantic similarity models to achieve new state-of-the-art results . |
| Approach: | They propose a hybrid neural network model for commonsense reasoning . it combines a masked language model and a semantic similarity model . |
| Outcome: | The proposed model outperforms the WNLI, WSC and PDP60 benchmarks on three commonsense reasoning tasks. |
Conceptual Similarity for Subjective Tags (2022.findings-aacl)
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| Challenge: | Existing methods of matching user queries with subjective tags rely on cosine similarity or semantic similarity models fail to recognize conceptual connections between tags. |
| Approach: | They propose a conceptual similarity pipeline to leverage conceptual awareness when assessing similarity between tags. |
| Outcome: | The proposed pipeline generates high-quality datasets and evaluates the model on a downstream application. |
Modeling Empathic Similarity in Personal Narratives (2023.emnlp-main)
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| Challenge: | EmpathicStories is a dataset of 1,500 personal stories annotated with empathic similarity features and 2,000 pairs of stories annnotated by empathism. |
| Approach: | They propose a task to identify similarity in personal stories based on empathic resonance . they use a dataset of 1,500 personal stories annotated with empathism features . |
| Outcome: | The proposed model outperforms semantic similarity models on correlation and retrieval metrics. |
tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection (2020.acl-main)
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| Challenge: | Recent pretrained contextual representations such as ELMo and BERT have led to impressive performance gains across a variety of NLP tasks, including semantic similarity detection. |
| Approach: | They propose a topic-informed BERT-based architecture for pairwise semantic similarity detection that adds topic information to pretrained contextual representations such as BERT. |
| Outcome: | The proposed model outperforms existing models on a variety of English language datasets and is highly performant. |