Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.
Code (1)
Tasks
Bayesian OptimisationCommunity Question AnsweringQuestion AnsweringReinforcement LearningSmall Data Image ClassificationSimilar Papers 제목 키워드 기반
Bayesian Deep Learning for Interactive Community Question Answering
Human-in-the-loop interactive learning has been shown to be effective for best solution selection tasks. Bayesian Optimisation (BO) reduces the amount of user interaction required but has so far relied on shallow models …
Bayesian OptimisationCommunity Question AnsweringDeep LearningQuestion AnsweringContextual Causal Bayesian Optimisation
Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored. While CaBO exploits ca…
Bayesian OptimisationMulti-Armed BanditsAn Empirical Study on Ensemble-Based Transfer Learning Bayesian Optimisation with Mixed Variable Types
Bayesian optimisation is a sample efficient method for finding a global optimum of expensive black-box objective functions. Historic datasets from related problems can be exploited to help improve performance of Bayesian…
Transfer LearningHeteroscedastic Treed Bayesian Optimisation
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global opti…
Bayesian OptimisationBIG-bench Machine LearningBatch Bayesian Optimization via Particle Gradient Flows
Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective functi…
Bayesian InferenceBayesian OptimisationBayesian Optimization