Papers with query representations
A Semi-supervised Scalable Unified Framework for E-commerce Query Classification (2025.acl-industry)
Copied to clipboard
Chunyuan Yuan, Chong Zhang, Zhen Fang, Ming Pang, Xue Jiang, Changping Peng, Zhangang Lin, Ching Law
| Challenge: | Existing query classification methods rely on posterior click behavior to construct training samples, resulting in insufficient prior information for modeling. |
| Approach: | They propose a semi-supervised scaleable unified framework that integrates enhanced modules to unify query classification tasks. |
| Outcome: | The proposed framework outperforms the state-of-the-art models in offline and online A/B experiments. |
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations (2021.acl-short)
Copied to clipboard
| Challenge: | Existing models of quotation recommendation ignore the relationship between quotations and queries. |
| Approach: | They propose a transformation matrix that directly maps quotations to quotation representations. |
| Outcome: | The proposed model outperforms state-of-the-art models on two datasets in English and Chinese. |
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (N19-1)
Copied to clipboard
| Challenge: | Existing Key-value Memory Neural Networks are effective for shallow reasoning over documents . but extending them to Knowledge Based Question Answering is not trivial . |
| Approach: | They propose a mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. |
| Outcome: | The proposed solution provides better reasoning abilities on complex questions and achieves state-of-the-art performance. |
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing fine-tuning techniques for information retrieval systems require learning query representations and query-document relations. |
| Approach: | They propose a method that bridges pre-training and fine-tuning by learning query representations and query-document relations in coarse-tuned models. |
| Outcome: | The proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. |
Optimizing Test-Time Query Representations for Dense Retrieval (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. |
| Approach: | They propose a test-time optimization of query representations that provides fine-grained pseudo labels over retrieval results. |
| Outcome: | The proposed algorithm improves open-domain question answering accuracy and direct re-ranking by up to 2.0% while running 1.3–2.4x faster with an efficient implementation. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
Copied to clipboard
Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
| Approach: | They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective. |
| Outcome: | The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks. |