Robust Learning for Multi-party Addressee Recognition with Discrete Addressee Codebook (2023)

Abstract: Addressee recognition aims to identify addressees in multi-party conversations. While state-of-the-art addressee recognition models have achieved promising performance, they still suffer from the issue of robustness when applied in real-world scenes. When exposed to a noisy environment, these models regard the noise as input and identify the addressee in a pre-given addressee closed set, while the addressees of the noise do not belong to this closed set, thus leading to the wrong identification of addressee. To this end, we propose a Robust Addressee Recognition (RAR) method, which discrete the addressees into a character codebook, making it able to represent open set addressees and robust in a noisy environment. Experimental results show that the introduction of the addressee character codebook helps to represent the open set addressees and highly improves the robustness of addressee recognition even if the input is noise.

## introduction different from two-party conversation, multiparty conversation has more than two interlocutors @xcite @xcite . beyond response generation or selection @xcite @xcite , there is also a need for recognizing the addressee of the multi-party conversation @xcite @xcite . addressee recognition aims to identify the interlocutors indicate to whom they are speaking. @xcite formalize the task as given a context to predict an addressee, the system is required to select an addressee appearing in the previous context. @xcite realize the importance of speaker modeling and propose speaker classification as a surrogate task for general speaker modeling. @xcite use what is your favorite food ? figure 1 : example of multi-party conversation in a noisy environment. a novel dialogue encoder to update speaker embeddings in a role-sensitive way. @xcite not only focuses on predicting the addressee of the last utterance but also aims to predict all the missing addressees. @xcite propose a unified multi-party pretrain model and design five selfsupervised tasks based on the interactions among utterances and interlocutors. these works suppose that the multi-party conversation happens in a quiet environment, which can lead to serious system failure when exposed to a noisy environment. many other works recently focus on robust learning in practice @xcite @xcite . however, these robust learning works mainly focus on two-party conversations and introduce noises by replacing, inserting, swapping, and deleting characters at the word level or words at the sentence level. the main difference between two-party conversation and multiparty conversation is that two-party conversation mainly focuses on perturbations at the semantic level, while beyond semantic perturbation, the multi-party conversation should consider the perturbations that are not intended for the current conversation, even if the noise is semantically complete. as shown in figure 1 , the noise is semantically complete but doesn't belong to the current conversation. since the number of addressees in a noisy environment is unknowable, giving a fixed length of the addressee matrix is not feasible. on account of the above issues, we propose the robust addressee recognition model (rarm), which discretizes the addressees into a codebook and represents addressees by addressee codes. we evaluate our method on two types of addressee noise: indomain addressee noise (id-an) and out-domain addressee noise (od-an). the id-an is the noise that has the same domain as the current multiparty conversation, and od-an is the noise that doesn't have. the main contributions are as follows: (1) we formalize the task of robust addressee recognition (rar) task in multi-party conversation and propose the robust addressee recognition model (rarm), which discretizes the addressees into a codebook, making addressee recognition robust in a noisy environment. (2) we conduct experiments on two types of noise: in-domain and out-domain noise, experimental results show that the addressee codebook helps to represent the addressees in noise effectively and highly improves the robustness of addressee recognition even if the input is in-domain or out-domain noise. ## conclusion in this paper, to improve the robustness of multiparty addressee recognition, we formalize the robust addressee recognition (rar) task and propose the robust addressee recognition model (rarm), which discretizes the addressees into a codebook, making it able to represent addressees in noise. we evaluate our method in two types of addressee noise: id-an and od-an. experimental results demonstrate that the addressee codebook helps to represent the addressees in noise effectively and highly improves the robustness of addressee recognition even if the input is indomain or out-domain noise.