BanditRank:Learning To Rank Using Contextual Bandits
Phanideep Gampa (IIT), Sumio Fujita
自然言語処理 (Natural Language Processing) 情報検索 (Information Retrieval) 機械学習 (Machine Learning)
- In this paper, we propose a novel extensible deep learning framework which uses reinforcement learning to train neural networks for ranking in retrieval tasks. We name our approach BanditRank as it treats ranking as a contextual bandit problem. In the domain of learning to rank for Information Retrieval (IR), the proposed deep learning models till now are trained on objective functions different from the metrics they’re evaluated on. Since most of the evaluation metrics are discrete quantities, they cannot be leveraged by the Gradient Descent algorithms directly without an approximation. The proposed framework bridges this gap by directly optimizing the task specific score such as Mean Average Precision (MAP) using gradient descent. Specifically, we propose a framework in which a contextual bandit whose action is to rank input documents is trained using policy gradient algorithm to directly maximize the reward. The reward can be a single metric like MAP or combination of several metrics. Also, the notion of ranking is inherent in the proposed framework similar to the existing listwise frameworks. To prove the effectiveness of BanditRank, we conduct a series of experiments on datasets related to three different tasks namely Web Search, Community and Factoid Question Answering. We demonstrate that it achieves results better than the state-of-the-art models when applied on the question answering datasets. On the web search dataset, we demonstrate that BanditRank achieves better result than the four strong listwise baselines used.
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