# Explainable Product Classification for Customs

arXiv: https://arxiv.org/abs/2311.10922 · HTML: https://mlsanity.com/paper/2311.10922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [QASC: A Dataset for Question Answering via Sentence Composition](https://mlsanity.com/paper/1910.11473/) (2019)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Hierarchical Graph Network for Multi-hop Question Answering](https://mlsanity.com/paper/1911.03631/) (2019)
- [Hyperbolic Interaction Model For Hierarchical Multi-Label Classification](https://mlsanity.com/paper/1905.10802/) (2019)
- [A Simple Yet Strong Pipeline for HotpotQA](https://mlsanity.com/paper/2004.06753/) (2020)
- [Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering](https://mlsanity.com/paper/2005.01218/) (2020)
- [Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts](https://mlsanity.com/paper/2210.13836/) (2022)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Evidence Sentence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/1902.08852/) (2019)
- [Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks](https://mlsanity.com/paper/1910.00290/) (2019)
- [Self-interpretable Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/2105.08589/) (2021)
- [iCapsNets: Towards Interpretable Capsule Networks for Text Classification](https://mlsanity.com/paper/2006.00075/) (2020)
- [Active Learning for Human-in-the-Loop Customs Inspection](https://mlsanity.com/paper/2010.14282/) (2020)
- [Customs Fraud Detection in the Presence of Concept Drift](https://mlsanity.com/paper/2109.14155/) (2021)
- [Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents](https://mlsanity.com/paper/1608.01972/) (2016)
- [Classification of Goods Using Text Descriptions With Sentences Retrieval](https://mlsanity.com/paper/2111.01663/) (2021)
- [Interpretable Text Classification Using CNN and Max-pooling](https://mlsanity.com/paper/1910.11236/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions](https://mlsanity.com/paper/2605.14857/) (2026)
- [Consensus-based Agentic Large Language Model Framework for Harmonized Tariff Schedule Code Classification](https://mlsanity.com/paper/2606.16987/) (2026)

## Abstract (arXiv)

The task of assigning internationally accepted commodity codes (aka HS codes) to traded goods is a critical function of customs offices. Like court decisions made by judges, this task follows the doctrine of precedent and can be nontrivial even for experienced officers. Together with the Korea Customs Service (KCS), we propose a first-ever explainable decision supporting model that suggests the most likely subheadings (i.e., the first six digits) of the HS code. The model also provides reasoning for its suggestion in the form of a document that is interpretable by customs officers. We evaluated the model using 5,000 cases that recently received a classification request. The results showed that the top-3 suggestions made by our model had an accuracy of 93.9% when classifying 925 challenging subheadings. A user study with 32 customs experts further confirmed that our algorithmic suggestions accompanied by explainable reasonings, can substantially reduce the time and effort taken by customs officers for classification reviews.