DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

Tang, Zhiwen; Yang, Grace Hui

doi:10.1609/aaai.v33i01.3301289

Cited by 13 publications

(11 citation statements)

References 21 publications

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“…Neural ranking models have been applied to ad-hoc retrieval [23,24], community-based QA [25], conversational search [26], and so on. Researchers began to go beyond the architecture of neural ranking models, paying attention to new training paradigms of neural ranking models [27], alternate indexing schemes for neural representations [28], integration of external knowledge [29,30], and other novel uses of neural approaches for IR tasks [31,32].…”

Section: Introductionmentioning

confidence: 99%

A Deep Look into neural ranking models for information retrieval

Guo

Fan

Pang

et al. 2020

Information Processing & Management

265

147

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Ranking models lie at the heart of research on information retrieval (IR). During the past decades, different techniques have been proposed for constructing ranking models, from traditional heuristic methods, probabilistic methods, to modern machine learning methods. Recently, with the advance of deep learning technology, we have witnessed a growing body of work in applying shallow or deep neural networks to the ranking problem in IR, referred to as neural ranking models in this paper. The power of neural ranking models lies in the ability to learn from the raw text inputs for the ranking problem to avoid many limitations of hand-crafted features. Neural networks have sufficient capacity to model complicated tasks, which is needed to handle the complexity of relevance estimation in ranking. Since there have been a large variety of neural ranking models proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we will take a deep look into the neural ranking models from different dimensions to analyze their underlying assumptions, major design principles, and learning strategies. We compare these models through benchmark tasks to obtain a comprehensive empirical understanding of the existing techniques. We will also discuss what is missing in the current literature and what are the promising and desired future directions.

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Section: Introductionmentioning

confidence: 99%

A Deep Look into neural ranking models for information retrieval

Guo

Fan

Pang

et al. 2020

Information Processing & Management

265

147

View full text Add to dashboard Cite

show abstract

“…A CNN is used in multiple interaction focused models including (Dai et al 2018;Hui et al 2017;Jaech et al 2017;McDonald et al 2018;Nie et al 2018;Tang and Yang 2019). Hu et al (2014) presented ARC-II which is an interaction-based method.…”

Section: Cnn-based Ranking Modelsmentioning

confidence: 99%

Neural ranking models for document retrieval

Trabelsi,

Chen,

Davison

et al. 2021

Preprint

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Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

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“…The latest advances in Neural Information Retrieval (NeuIR) have demonstrated the effectiveness of using topical structures for NeuIR (Tang and Yang 2019;Fan et al 2018). In this work, we follow (Tang and Yang 2019) for segmenting and standardizing documents. Each document is split into a fixed B number of segments (B is empirically set to 20).…”

Section: Build a Global Representationmentioning

confidence: 99%

Corpus-Level End-to-End Exploration for Interactive Systems

Tang¹,

Yang²

2020

AAAI

Self Cite

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A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.

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DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

Cited by 13 publications

References 21 publications

A Deep Look into neural ranking models for information retrieval

A Deep Look into neural ranking models for information retrieval

Neural ranking models for document retrieval

Corpus-Level End-to-End Exploration for Interactive Systems

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