Beyond

By D. Battaglia. 2020

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In Optimization & Training

Citations in the corpus

Autoregressive Search Engines: Generating Substrings as Document Identifiers (2022, background): “…strand of research is that of query likelihood models, which, in their latest incarnations, use autoregressive models to (re)rank passages according to the probability P ( q | p ) of a query q given the passage p (Nogueira dos Santos et al., 2020; Zhuang and Zuccon, 2021; Lesota et al., 2021).”
Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “This limitation is addressed by a number of models [1, 4, 20, 25, 30, 39], and a simple retrieve-then-rerank approach can be elaborated into a multi-stage architecture with reranker pipelines [26, 33, 37] that balance effectiveness and efficiency.”
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks (2018, neutral): “Meyerson and Miikkulainen (2017) introduced as a method for learning how to apply layers in different ways at different depths for different tasks, while simultaneously learning the layers themselves.”
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models (2021, unknown): “In a larger context, neural language generation models span various language processing tasks, i.e. machine translation [47], abstrac-tive document summarization [24, 43], dialogue generation [48], and question answering [10, 49].”
Improving Passage Retrieval with Zero-Shot Question Generation (2022, methodology): “More recent work on re-rankers have fine-tuned PLMs on question-passage pairs to generate relevance labels (Nogueira et al., 2020), some-times to jointly generate question and relevance labels (Nogueira dos Santos et al., 2020; Ju et al., 2021).”
TILDE: Term Independent Likelihood moDEl for Passage Re-ranking (2021, unknown): “…Efficiency Representation Based [12, 16, 31] ++ + Modified Document [6, 20] + +++ Deep LMs Re-ranker [18, 19] +++ - - Deep QLMs Re-ranker [8, 33] ++ - - - TILDE (ours) ++ +++ Unlike traditional query likelihood models [29, 30] and deep query likelihood models [8, 33] which only optimise…”
A Survey of Model Architectures in Information Retrieval (2025, unknown): “These challenges drive opportunities for developing more adaptive, efficient, scalable and intelligent retrieval systems.”