{"url":"/method/bp-transformer","slug":"bp-transformer","name":"BP-Transformer","full_name":"BP-Transformer","full_name_withheld":false,"description_markdown":"The **BP-Transformer (BPT)** is a type of [Transformer](https://paperswithcode.com/method/transformer) that is motivated by the need to find a better balance between capability and computational complexity for self-attention. The architecture partitions the input sequence into different multi-scale spans via binary partitioning (BP). It incorporates an inductive bias of attending the context information from fine-grain to coarse-grain as the relative distance increases. The farther the context information is, the coarser its representation is.\r\nBPT can be regard as graph neural network, whose nodes are the multi-scale spans. A token node can attend the smaller-scale span for the closer context and the larger-scale span for the longer distance context. The representations of nodes are updated with [Graph Self-Attention](https://paperswithcode.com/method/graph-self-attention).","description_state":"present","introduced_year":null,"introduced_by":{"title":"BP-Transformer: Modelling Long-Range Context via Binary Partitioning","paper":"/paper/bp-transformer-modelling-long-range-context","first_author":"Zihao Ye","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/bp-transformer-modelling-long-range-context"},"source":{"url":"https://arxiv.org/abs/1911.04070v1","title":"BP-Transformer: Modelling Long-Range Context via Binary Partitioning","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Transformers","url":"/methods/category/transformers","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/bp-transformer-modelling-long-range-context","title":"BP-Transformer: Modelling Long-Range Context via Binary Partitioning","date":"2019-11-11","arxiv_id":"1911.04070","n_code_links":2,"syntology":{"ran":0,"of":7,"unverified":7,"pointer_only":0}}],"papers_shown":1,"tasks":[{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/machine-translation","name":"Machine Translation","papers":1},{"task":"/task/sentiment-analysis","name":"Sentiment Analysis","papers":1},{"task":"/task/text-classification","name":"Text Classification","papers":1},{"task":"/task/translation","name":"Translation","papers":1},{"task":"/task/text-classification-1","name":"text-classification","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2019","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/bp-transformer"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}