{"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/visualizing-and-understanding-neural-models","title":"Visualizing and Understanding Neural Models in NLP","arxiv_id":"1506.01066","date":"2015-06-02","proceeding":"NAACL 2016 6","authors":["Jiwei Li","Xinlei Chen","Eduard Hovy","Dan Jurafsky"],"abstract":"While neural networks have been successfully applied to many NLP tasks the\nresulting vector-based models are very difficult to interpret. For example it's\nnot clear how they achieve {\\em compositionality}, building sentence meaning\nfrom the meanings of words and phrases. In this paper we describe four\nstrategies for visualizing compositionality in neural models for NLP, inspired\nby similar work in computer vision. We first plot unit values to visualize\ncompositionality of negation, intensification, and concessive clauses, allow us\nto see well-known markedness asymmetries in negation. We then introduce three\nsimple and straightforward methods for visualizing a unit's {\\em salience}, the\namount it contributes to the final composed meaning: (1) gradient\nback-propagation, (2) the variance of a token from the average word node, (3)\nLSTM-style gates that measure information flow. We test our methods on\nsentiment using simple recurrent nets and LSTMs. Our general-purpose methods\nmay have wide applications for understanding compositionality and other\nsemantic properties of deep networks , and also shed light on why LSTMs\noutperform simple recurrent nets,","url_abs":"http://arxiv.org/abs/1506.01066v2","url_pdf":"http://arxiv.org/pdf/1506.01066v2.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":[{"paper_slug":"visualizing-and-understanding-neural-models","repo_url":"https://github.com/phnk/D7047E","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"negation","task_name":"Negation"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.01066","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}