{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/distance-based-self-attention-network-for","title":"Distance-based Self-Attention Network for Natural Language Inference","arxiv_id":"1712.02047","date":"2017-12-06","proceeding":null,"authors":["Jinbae Im","Sungzoon Cho"],"abstract":"Attention mechanism has been used as an ancillary means to help RNN or CNN.\nHowever, the Transformer (Vaswani et al., 2017) recently recorded the\nstate-of-the-art performance in machine translation with a dramatic reduction\nin training time by solely using attention. Motivated by the Transformer,\nDirectional Self Attention Network (Shen et al., 2017), a fully attention-based\nsentence encoder, was proposed. It showed good performance with various data by\nusing forward and backward directional information in a sentence. But in their\nstudy, not considered at all was the distance between words, an important\nfeature when learning the local dependency to help understand the context of\ninput text. We propose Distance-based Self-Attention Network, which considers\nthe word distance by using a simple distance mask in order to model the local\ndependency without losing the ability of modeling global dependency which\nattention has inherent. Our model shows good performance with NLI data, and it\nrecords the new state-of-the-art result with SNLI data. Additionally, we show\nthat our model has a strength in long sentences or documents.","url_abs":"http://arxiv.org/abs/1712.02047v1","url_pdf":"http://arxiv.org/pdf/1712.02047v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"Distance-based Self-Attention Network","rank_in_archive_order":57,"of":98,"metrics":{"% Test Accuracy":"86.3","% Train Accuracy":"89.6","Parameters":"4.7m"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.02047","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}