Accurate evaluation of conversational retrieval is pivotal for advancing Retrieval-Augmented Generation (RAG) systems. However, existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. To address these challenges, we introduce MTR-Suite, a unified framework for auditing, synthesizing, and benchmarking retrieval. It features: (1) MTR-Eval, an LLM-based auditor quantifying alignment gaps in previous benchmarks; (2) MTR-Pipeline, a multi-agent system using greedy traversal clustering to generate high-fidelity dialogues at 1/400th human cost; and (3) MTR-Bench, a rigorous general-domain benchmark. MTR-Bench mimics production-style challenges (hard topic switching, verbosity), offering superior discriminative power. We make our code and data publicly available to facilitate future research.

Neural machine translation (NMT) has advanced significantly, yet challenges remain in adapting to new domains . In scenarios where bilingual data is limited, this issue is further exacerbated. To address this, we propose kNN-LM-NMT, a method that leverages semantically similar target language sentences in the kNN framework. Our approach generates a probability distribution over these sentences during decoding, and this distribution is then interpolated with the NMT model’s distribution. Additionally, we introduce an n-gram-based approach to focus on similar fragments, enabling the model to avoid the noise introduced by the non-similar parts. To enhance accuracy, we further incorporate cross-lingual retrieval similarity to refine the kNN probability distribution. Extensive experiments on multi-domain datasets demonstrate significant performance improvements in both high-resource and low-resource scenarios. Our approach effectively extracts translation knowledge from limited target domain data, and well benefits from large-scale monolingual data for robust context representation.