Intricacies of Collins\u27 Parsing Model

This paper documents a large set of heretofore unpublished details Collins used in his parser, such that, along with Collins\u27 thesis (Collins, 1999), this paper contains all information necessary to duplicate Collins\u27 benchmark results. Indeed, these as-yet-unpublished details account for an 11% relative increase in error from an implementation including all details to a clean-room implementation of Collins\u27 model. We also show a cleaner and equally-well-performing method for the handling of punctuation and conjunction, and reveal certain other probabilistic oddities about Collins\u27 parser. We analyze not only the effect of the unpublished details, but also reanalyze the effect of certain well-known details, revealing that bilexical dependencies are barely used by the model and that head choice is not nearly as important to overall parsing performance as once thought. Finally, we perform experiments that show that the true discriminative power of lexicalization appears to lie in the fact that unlexicalized syntactic structures are generated conditioning on the head word and its part of speech

Step by Step to Fairness: Attributing Societal Bias in Task-oriented Dialogue Systems

Recent works have shown considerable improvements in task-oriented dialogue (TOD) systems by utilizing pretrained large language models (LLMs) in an end-to-end manner. However, the biased behavior of each component in a TOD system and the error propagation issue in the end-to-end framework can lead to seriously biased TOD responses. Existing works of fairness only focus on the total bias of a system. In this paper, we propose a diagnosis method to attribute bias to each component of a TOD system. With the proposed attribution method, we can gain a deeper understanding of the sources of bias. Additionally, researchers can mitigate biased model behavior at a more granular level. We conduct experiments to attribute the TOD system's bias toward three demographic axes: gender, age, and race. Experimental results show that the bias of a TOD system usually comes from the response generation model