Lexicon-based retrieval has gained siginificant popularity in text retrieval
due to its efficient and robust performance. To further enhance performance of
lexicon-based retrieval, researchers have been diligently incorporating
state-of-the-art methodologies like Neural retrieval and text-level contrastive
learning approaches. Nonetheless, despite the promising outcomes, current
lexicon-based retrieval methods have received limited attention in exploring
the potential benefits of feature context representations and term-level
knowledge guidance. In this paper, we introduce an innovative method by
introducing FEature Context and TErm-level Knowledge modules(FecTek). To
effectively enrich the feature context representations of term weight, the
Feature Context Module (FCM) is introduced, which leverages the power of BERT's
representation to determine dynamic weights for each element in the embedding.
Additionally, we develop a term-level knowledge guidance module (TKGM) for
effectively utilizing term-level knowledge to intelligently guide the modeling
process of term weight. Evaluation of the proposed method on MS Marco benchmark
demonstrates its superiority over the previous state-of-the-art approaches.

通过引入特征上下文和术语级知识模块（FecTek），该论文通过引入 FCM 和 TKGM 模块来改进基于词典的检索方法，提高性能，并在 MS Marco 基准测试中展现出优越性能。

FecTek：利用特征上下文和术语级知识增强基于词典的检索中的术语权重

FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature  Context and Term-level Knowledge

Adversarial attacks have gained traction in order to identify potential
vulnerabilities in neural ranking models (NRMs), but current attack methods
often introduce grammatical errors, nonsensical expressions, or incoherent text
fragments, which can be easily detected. Additionally, current methods rely
heavily on the use of a well-imitated surrogate NRM to guarantee the attack
effect, which makes them difficult to use in practice. To address these issues,
we propose a framework called Imperceptible DocumEnt Manipulation (IDEM) to
produce adversarial documents that are less noticeable to both algorithms and
humans. IDEM instructs a well-established generative language model, such as
BART, to generate connection sentences without introducing easy-to-detect
errors, and employs a separate position-wise merging strategy to balance
relevance and coherence of the perturbed text. Experimental results on the
popular MS MARCO benchmark demonstrate that IDEM can outperform strong
baselines while preserving fluency and correctness of the target documents as
evidenced by automatic and human evaluations. Furthermore, the separation of
adversarial text generation from the surrogate NRM makes IDEM more robust and
less affected by the quality of the surrogate NRM.

本研究提出了一种名为 Imperceptible DocumEnt Manipulation (IDEM) 的框架，该框架可以生成对算法和人类来说更难察觉的对抗性文档，并通过在不引入易于检测错误的情况下指导已建立的生成语言模型（例如 BART）生成连接语句，同时采用分离的位置合并策略来平衡扰动文本的相关性和一致性。实验结果表明，IDEM 可以在保持目标文档的流畅性和正确性的同时，胜过强的基准，并且将对抗文本生成与替代 NRM 的分离使 IDEM 更加强健，不受替代 NRM 质量的影响。