@inproceedings{li-etal-2022-human,
title = "Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation",
author = "Li, Raymond and
Xiao, Wen and
Xing, Linzi and
Wang, Lanjun and
Murray, Gabriel and
Carenini, Giuseppe",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.694",
doi = "10.18653/v1/2022.emnlp-main.694",
pages = "10189--10204",
abstract = "The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns. Then those patterns are injected, not only to smaller models, but also to the original model. The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation. After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency.",
}
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%0 Conference Proceedings
%T Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
%A Li, Raymond
%A Xiao, Wen
%A Xing, Linzi
%A Wang, Lanjun
%A Murray, Gabriel
%A Carenini, Giuseppe
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-human
%X The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns. Then those patterns are injected, not only to smaller models, but also to the original model. The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation. After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency.
%R 10.18653/v1/2022.emnlp-main.694
%U https://aclanthology.org/2022.emnlp-main.694
%U https://doi.org/10.18653/v1/2022.emnlp-main.694
%P 10189-10204
Markdown (Informal)
[Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation](https://aclanthology.org/2022.emnlp-main.694) (Li et al., EMNLP 2022)
ACL