Neural Ranking Models with Weak Supervision (1704.08803v2)

Abstract: Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.

Neural Ranking Models with Weak Supervision

The paper "Neural Ranking Models with Weak Supervision" investigates leveraging weak supervision to enhance neural ranking models in information retrieval (IR). The authors propose a methodology where unsupervised ranking models such as BM25 provide weak supervisory signals to train neural rankers, enabling the modeling of complex ranking interactions without direct human-labeled data.

Overview of Methodology

The central hypothesis is that neural ranking models can be effectively trained using labels derived from traditional unsupervised IR models. The paper explores various neural architectures, including point-wise and pair-wise models, and evaluates their performance under different scenarios. The researchers utilize three primary input representation methods: dense vector, sparse vector, and embedding vector representations to encapsulate query-document relationships.

Experimental Setup and Results

Over six million queries from AOL logs were utilized for training, with evaluations conducted on Robust04 and ClueWeb09 collections. These evaluations utilized metrics such as Mean Average Precision (MAP), P@20, and nDCG@20. Remarkably, the best-performing neural model achieved more than 13% and 35% MAP improvements over BM25 on the Robust04 and ClueWeb collections, respectively.

Key findings include the observation that embedding vector representations, which allow the network to learn optimized feature representations, significantly outperform dense and sparse vector approaches. Moreover, pair-wise models trained with ranking objectives often surpass point-wise models, underscoring the importance of focusing on learning document preferences over calibrated scoring.

Implications and Future Directions

The paper offers substantial insights into the practical application of weak supervision in IR. The approach allows for the deployment of neural models where annotated data is scarce, bridging the gap between classic IR models and modern deep learning techniques. An exciting extension of this work would be to examine the efficacy of advanced architectures like convolutional and recurrent networks under weak supervision. Additionally, leveraging multiple weak supervision signals could yield richer training datasets, further enhancing model generalization.

The suggested paradigm also holds promise beyond pure ad-hoc retrieval, potentially extending to tasks such as document classification or filtering where supervised datasets are limited. Exploring such applications could significantly broaden the impact of weak supervision in the IR landscape.

Overall, the paper makes a compelling case for the integration of traditional unsupervised methods with data-driven neural approaches, paving the way for more effective and scalable solutions in information retrieval.