# Technical Question Answering across Tasks and Domains

arXiv: https://arxiv.org/abs/2010.09780 · HTML: https://mlsanity.com/paper/2010.09780/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations](https://mlsanity.com/paper/2106.02974/) (2021)
- [Insert or Attach: Taxonomy Completion via Box Embedding](https://mlsanity.com/paper/2305.11004/) (2023)
- [Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains](https://mlsanity.com/paper/2401.13129/) (2024)
- [Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questions](https://mlsanity.com/paper/2606.03331/) (2026)

## Abstract (arXiv)

Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.