Papers by Lucas Moeller
Similar, but why? A Toolkit for Explaining Text Similarity (2026.eacl-demo)
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| Challenge: | XPLAINSIM is a Python package that explains textual similarity in an easy-to-use way. |
| Approach: | They propose a Python package that unifies three approaches to explain text similarity . they demonstrate the value of the package through intuitive examples and empirical research . |
| Outcome: | XPLAINSIM is a Python package that unifies three approaches to explain text similarity . the authors show that the package is useful for explaining text similarities in a simple way . |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |
An Attribution Method for Siamese Encoders (2023.emnlp-main)
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| Challenge: | Despite the success of Siamese encoder models, little is known about the aspects of inputs they base their decisions on. |
| Approach: | They propose a local attribution method for Siamese encoders by generalizing the principle of integrated gradients to models with multiple inputs. |
| Outcome: | The proposed method can be reduced to a token–token matrix and account for the model’s full computation graph and is guaranteed to converge to the actual prediction. |
Approximate Attributions for Off-the-Shelf Siamese Transformers (2024.eacl-long)
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| Challenge: | Existing attribution methods cannot tackle Siamese encoders since they compare two inputs rather than processing a single one. |
| Approach: | They propose an attribution method specifically targeted for Siamese encoders that can be adjusted and fine-tuned to retain original model's predictive performance. |
| Outcome: | The proposed method retains the original model's predictive performance and can be applied to off-the-shelf models. |