Papers with TechQA
Technical Question Answering across Tasks and Domains (2021.naacl-industry)
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| Challenge: | Existing methods for technical QA have a limited data size and question and answer overlaps . |
| Approach: | They propose a framework of deep transfer learning to address technical QA across tasks and domains using document retrieval and reading comprehension tasks. |
| Outcome: | The proposed framework performs better than state-of-the-art methods on the TechQA task. |
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems (2025.acl-industry)
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| Challenge: | Existing methods for lexical retrieval struggle due to semantic mismatches and overlapping terminologies, and ambiguous abbreviations common in specialized fields like finance and cloud computing. |
| Approach: | They propose a scalable hard-negative mining framework that dynamically selects semantically challenging but contextually irrelevant documents to enhance deployed re-ranking models. |
| Outcome: | The proposed framework improves on public domain datasets and shows that it is generalizable and ready for real-world applications. |
VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension (2021.acl-short)
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| Challenge: | Existing models on Machine Reading Comprehension (MRC) require complex model architecture for effectively modeling long texts with paragraph representation and classification, making inference computationally inefficient for production use. |
| Approach: | They propose a novel Gaussian distribution-based paragraph representation for Machine Reading Comprehension (MRC) that is light-weight and parallel-efficient. |
| Outcome: | The proposed model can achieve comparable performance on Wikipedia-based (NQ) and TechNotes (TechQA) with a state-of-the-art (SOTA) complex document modeling approach while being 16 times faster, demonstrating the efficiency of the proposed model. |