Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search (CIKM 2024)
- Venue: CIKM 2024
- Link: https://dl.acm.org/doi/abs/10.1145/3627673.3679534
- Topic area: Multi-turn / conversational search, dense retrieval
- Targets a core problem in conversational search: user queries in later turns are often elliptical and context-dependent, making direct dense retrieval unreliable.
- Proposes aligning the current query's representation with the rewritten query (a self-contained reformulation) and with relevance judgments, using these as supervision/alignment signals for the query encoder.
- The goal is to make dense retrievers robust to conversational context without depending entirely on an explicit query rewriting module at inference time.
- Datasets: standard conversational search benchmarks (per the original paper's tables).
- Metrics: ranking quality measures such as nDCG, MRR, Recall@k.
- Baselines: BM25, dense retrieval without conversational modeling, and rewrite-based pipelines.
- CHIQ: Contextual History Enhancement for Improving Query Rewriting
- Few-Shot Conversational Dense Retrieval (SIGIR 2021)
- Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Understanding
- Original paper: *Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search*, CIKM 2024. ACM DL
Key points
Background
In large-scale conversational search systems, traditional pipelines separate retrieval, ranking, and generation, which complicates multi-turn intent understanding. This work addresses how to encode conversational queries so that retrieval remains effective across turns, in line with the broader move toward LLM-assisted conversational IR.
Method outline
The typical pipeline follows: problem formulation → model design → training procedure → inference.
1. Input & representation: encode the conversational query (with dialogue history) into a dense embedding. 2. Alignment objectives: train the representation to be close to the rewritten query representation and consistent with relevance labels. 3. Learning strategy: supervised/contrastive training using rewrite supervision and relevance judgments. 4. Inference: standard dense retrieval over a prebuilt index, avoiding costly per-turn rewriting.
Evaluation
Exact numerical results are not reproduced here; consult the original PDF before citing quantitative claims.
Takeaways
1. Alignment signals from query rewrites and relevance judgments can compensate for noisy conversational context in dense retrieval. 2. Reducing reliance on explicit rewriting improves latency and system simplicity in multi-turn search. 3. Open questions include cross-domain generalization, evaluation of multi-turn consistency, and the trade-off between rewrite quality and retrieval cost.