tailieunhanh - Báo cáo khoa học: "Modeling Wisdom of Crowds Using Latent Mixture of Discriminative Experts"

In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as ”wisdom of crowds”. In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidden dynamic among different experts. Experiments show improvement over state-of-the-art approaches on the task of listener backchannel prediction in dyadic conversations. . | Modeling Wisdom of Crowds Using Latent Mixture of Discriminative Experts Derya Ozkan and Louis-Philippe Morency Institute for Creative Technologies University of Southern California ozkan morency @ Abstract In many computational linguistic scenarios training labels are subjectives making it necessary to acquire the opinions of multiple an-notators experts which is referred to as wisdom of crowds . In this paper we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts LMDE model that can automatically learn the prototypical patterns and hidden dynamic among different experts. Experiments show improvement over state-of-the-art approaches on the task of listener backchannel prediction in dyadic conversations. 1 Introduction In many real life scenarios it is hard to collect the actual labels for training because it is expensive or the labeling is subjective. To address this issue a new direction of research appeared in the last decade taking full advantage of the wisdom of crowds Surowiecki 2004 . In simple words wisdom of crowds enables parallel acquisition of opinions from multiple annotators experts. In this paper we propose a new method to fuse wisdom of crowds. Our approach is based on the Latent Mixture of Discriminative Experts LMDE model originally introduced for multimodal fusion Ozkan et al. 2010 . In our Wisdom-LMDE model a discriminative expert is trained for each crowd member. The key advantage of our computational model is that it can automatically discover the prototypical patterns of experts and learn the dynamic between these patterns. An overview of our approach is depicted in Figure 1. 335 We validate our model on the challenging task of listener backchannel feedback prediction in dyadic conversations. Backchannel feedback includes the nods and paraverbals such as uh-huh and mm-hmm that listeners produce as they are speaking. Backchannels play a significant role in determining the nature