24 research outputs found
Reinforcement learning for combining relevance feedback techniques
Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user’s feedback history. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions also provides significant contributions for improvement. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model against a growing-size database
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
The dual-encoder has become the de facto architecture for dense retrieval.
Typically, it computes the latent representations of the query and document
independently, thus failing to fully capture the interactions between the query
and document. To alleviate this, recent research has focused on obtaining
query-informed document representations. During training, it expands the
document with a real query, but during inference, it replaces the real query
with a generated one. This inconsistency between training and inference causes
the dense retrieval model to prioritize query information while disregarding
the document when computing the document representation. Consequently, it
performs even worse than the vanilla dense retrieval model because its
performance heavily relies on the relevance between the generated queries and
the real query.In this paper, we propose a curriculum sampling strategy that
utilizes pseudo queries during training and progressively enhances the
relevance between the generated query and the real query. By doing so, the
retrieval model learns to extend its attention from the document alone to both
the document and query, resulting in high-quality query-informed document
representations. Experimental results on both in-domain and out-of-domain
datasets demonstrate that our approach outperforms previous dense retrieval
models.Comment: Accetpted to EMNLP 202
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong
teacher model to a relatively weak student model. Traditional knowledge
distillation methods include response-based methods and feature-based methods.
Response-based methods are used the most widely but suffer from lower upper
limit of model performance, while feature-based methods have constraints on the
vocabularies and tokenizers. In this paper, we propose a tokenizer-free method
liberal feature-based distillation (LEAD). LEAD aligns the distribution between
teacher model and student model, which is effective, extendable, portable and
has no requirements on vocabularies, tokenizer, or model architecture.
Extensive experiments show the effectiveness of LEAD on several widely-used
benchmarks, including MS MARCO Passage, TREC Passage 19, TREC Passage 20, MS
MARCO Document, TREC Document 19 and TREC Document 20.Comment: Work in progres
BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver
Open-domain question answering is a crucial task that often requires
accessing external information. Existing methods typically adopt a single-turn
retrieve-then-read approach, where relevant documents are first retrieved, and
questions are then answered based on the retrieved information. However, there
are cases where answering a question requires implicit knowledge that is not
directly retrievable from the question itself. In this work, we propose a novel
question-answering pipeline called BeamSearchQA. Our approach leverages large
language models to iteratively generate new questions about the original
question, enabling an iterative reasoning process. By iteratively refining and
expanding the scope of the question, our method aims to capture and utilize
hidden knowledge that may not be directly obtainable through retrieval. We
evaluate our approach on the widely-used open-domain NQ and WebQ datasets. The
experimental results demonstrate that BeamSearchQA significantly outperforms
other zero-shot baselines, indicating its effectiveness in tackling the
challenges of open-domain question answering.Comment: Work in progres
Concept learning and transplantation for dynamic image databases
The task of a content-based image retrieval (CBIR) system is to cater to users who ezpect to get rele-vant images with high precision and eficiency in re-sponse to query images. This paper presents a concept learning approach that integrates a mixture model of the data, relevance feedback and long-term continuous learning. The concepts are incrementally refined with increased retrieval ezperiences. The concept knowl-edge can be immediately tmnsplanted to deal with the dynamic database situations such as insertion of new images, removal of ensting images and query images which are outside the database. Experimental results on Core1 database show the eficacy of our approach.
Evolutionary Feature Synthesis for Image Databases
The high dimensionality of visual features is one of the major challenges for content-based image retrieval (CBIR) systems, and a variety of dimensionality reduc-tion approaches have been proposed to find the discrim-inant features. In this paper, we investigate the effec-tiveness of coevolutionary genetic programming (CGP) in synthesizing feature vectors for image databases from traditional features that are commonly used. The trans-formation for feature dimensionality reduction by CGP has two unique characteristics for image retrieval: 1) nonlinearlity: CGP does not assume any class distri-bution in the original visual feature space; 2) explicit-ness: unlike kernel trick, CGP yields explicit transfor-mation for dimensionality reduction so that the images can be searched in the low-dimensional feature space. The experimental results on multiple databases show that (a) CGP approach has distinct advantage over the linear transformation approach of Multiple Discrimi-nant Analysis (MDA) in the sense of the discrimination ability of the low-dimensional features, and (b) the clas-sification performance using the features synthesized by our CGP approach is comparable to or even superior to that of support vector machine (SVM) approach using the original visual features.
Content-Aware Click Modeling
Click models aim at extracting intrinsic relevance of documents to queries from biased user clicks. One basic modeling assumption made in existing work is to treat such intrinsic relevance as an atomic query-document-specific parameter, which is solely estimated from historical clicks without using any content information about a document or relationship among the clicked/skipped documents under the same query. Due to this overly simplified assumption, existing click models can neither fully explore the information about a document’s relevance quality nor make predictions of relevance for any unseen documents. In this work, we proposed a novel Bayesian Sequential State model for modeling the user click behaviors, where the document content and dependencies among the sequential click events within a query are characterized by a set of descriptive features via a probabilistic graphical model. By applying the posterior regularized Expectation Maximization algorithm for parameter learning, we tailor the model to meet specific ranking-oriented properties, e.g., pairwise click preferences, so as to exploit richer information buried in the user clicks. Experiment results on a large set of real click logs demonstrate the effectiveness of the proposed model compared with several state-of-the-art click models