# Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications

arXiv: https://arxiv.org/abs/2306.16143 · HTML: https://mlsanity.com/paper/2306.16143/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval](https://mlsanity.com/paper/2112.07577/) (2021)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://mlsanity.com/paper/2104.06979/) (2021)
- [Offline A/B testing for Recommender Systems](https://mlsanity.com/paper/1801.07030/) (2018)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Semantic Sensitive TF-IDF to Determine Word Relevance in Documents](https://mlsanity.com/paper/2001.09896/) (2020)
- [Representation learning for very short texts using weighted word embedding aggregation](https://mlsanity.com/paper/1607.00570/) (2016)
- [Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models](https://mlsanity.com/paper/2102.00036/) (2021)
- [Evaluation of Interactive Machine Learning Systems](https://mlsanity.com/paper/1801.07964/) (2018)
- [Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF](https://mlsanity.com/paper/1710.06524/) (2017)
- [Improving a tf-idf weighted document vector embedding](https://mlsanity.com/paper/1902.09875/) (2019)

## Built on by (most cited first, 5 of 5)

- [Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language](https://mlsanity.com/paper/2504.19856/) (2025)
- [PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&A](https://mlsanity.com/paper/2602.21045/) (2026)
- [Link Prediction for Event Logs in the Process Industry](https://mlsanity.com/paper/2508.09096/) (2025)
- [An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systems](https://mlsanity.com/paper/2602.21059/) (2026)
- [Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry](https://mlsanity.com/paper/2510.04631/) (2025)

## Abstract (arXiv)

User experience (UX) is a part of human-computer interaction (HCI) research and focuses on increasing intuitiveness, transparency, simplicity, and trust for the system users. Most UX research for machine learning (ML) or natural language processing (NLP) focuses on a data-driven methodology. It engages domain users mainly for usability evaluation. Moreover, more typical UX methods tailor the systems towards user usability, unlike learning about the user needs first. This paper proposes a new methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating system usefulness and user utility. The methodology emerged from and is evaluated on a case study about the full-cycle prototype development of a domain-specific semantic search for daily operations in the process industry. A key finding of our case study is that involving domain experts increases their interest and trust in the final NLP application. The combined UX+NLP research of the proposed method efficiently considers data- and user-driven opportunities and constraints, which can be crucial for developing NLP applications.