{"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/explainable-inference-on-sequential-data-via","title":"Explainable Inference on Sequential Data via Memory-Tracking","arxiv_id":null,"date":"2020-07-11","proceeding":null,"authors":["Biagio La Rosa","Roberto Capobianco","Daniele Nardi"],"abstract":"In this paper we present a novel mechanism to\r\nget explanations that allow to better understand\r\nnetwork predictions when dealing with sequential\r\ndata. Specifically, we adopt memory-based networks — Differential Neural Computers — to exploit their capability of storing data in memory and\r\nreusing it for inference. By tracking both the memory access at prediction time, and the information\r\nstored by the network at each step of the input\r\nsequence, we can retrieve the most relevant input\r\nsteps associated to each prediction. We validate\r\nour approach (1) on a modified T-maze, which is a\r\nnon-Markovian discrete control task evaluating an\r\nalgorithm’s ability to correlate events far apart in\r\nhistory, and (2) on the Story Cloze Test, which is\r\na commonsense reasoning framework for evaluating story understanding that requires a system to\r\nchoose the correct ending to a four-sentence story.\r\nOur results show that we are able to explain agent’s\r\ndecisions in (1) and to reconstruct the most relevant\r\nsentences used by the network to select the story\r\nending in (2). Additionally, we show not only that\r\nby removing those sentences the network prediction changes, but also that the same are sufficient to\r\nreproduce the inference.","url_abs":"https://www.ijcai.org/Proceedings/2020/278","url_pdf":"https://www.ijcai.org/Proceedings/2020/0278.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":"explainable-inference-on-sequential-data-via","repo_url":"https://github.com/KRLGroup/explainable-inference-on-sequential-data-via-memory-tracking","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cloze-test","task_name":"Cloze Test"},{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"content-based-attention","method_name":"Content-based Attention"},{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}