Unsupervised Conversation Disentanglement through Co-Training (2021)

Abstract: Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which is expensive to obtain in practice. In this work, we explore training a conversation disentanglement model without referencing any human annotations. Our method is built upon the deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible of retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both the two networks are initialized respectively with pseudo data built from the unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection.

## introduction with the continuing growth of internet and social media, online group chat channels, e.g., slack 1 and whatsapp 2 , among many others, have become increasingly popular and played a significant social and economic role. along with the convenience of instant communication brought by these applications, the inherent property that multiple topics are often discussed in one channel hinders an efficient access to the conversational content. in the example shown in figure 1 , people or intelligent systems have to selectively read the messages related to the topics they are interested in from hundreds of messages in the chat channel. with the goal of automatically grouping messages with the same topic into one session, conversation disentanglement has proved to be a prerequisite for understanding multi-party conversations and solving the corresponding downstream tasks such as response selection @xcite @xcite . previous research on conversation disentanglement can be roughly divided into two categories: though end-to-end methods have been proved to be more flexible and can achieve better performance @xcite , these two types of methods are interconnected and complementary since a global optimal clustering solution on the local relations will produce the optimal disentanglement scheme @xcite . although the previous research efforts have achieved an impressive progress on conversation disentanglement, they all highly rely on humanannotated corpora, which are expensive and scarce to obtain in practice @xcite . the heavy dependence on human annotations limits the extensions of related study on conversation disentanglement as well as the applications on downstream tasks, given a wide variety of occasions where multi-party conversations can happen. in this work, we explore the possibility to train an endto-end conversation disentanglement model without referencing any human annotations and propose a completely unsupervised disentanglement model. our method builds upon the co-training approach @xcite but extends it to a deep learning framework. by viewing the disentanglement task from the local perspective and the global perspective, our method consists of a message-pair classifier and a session classifier. the message-pair classifier aims to retrieve the message pair relations, which is of a similar purpose as the model used in a two-step method that retrieves the local relations between two messages. the session classifier is a global context-aware model that can directly categorize a message into a session in an end-to-end fashion. the two classifiers view the task of conversation disentanglement from the perspectives of a local two-step method and an global end-to-end model, which will be separately initialized with pseudo data built from the unannotated corpus and updated with each other during co-training. more concretely, during the co-training procedure, we adopt reinforcement learning to learn a session assigning policy for the session classifier by maximizing the accumulated rewards between a message and a session which are given by the message-pair classifier. after updating the parameters of the session classifier, a new set of data with high confidence will be retrieved from the predicted disentanglement results of the session classifier and used for updating the message-pair classifier. as shown in figure 2 , the above process is iteratively performed by updating one classifier with the other until the performance of session classifier stops increasing. we conduct experiments on the large public movie dialogue dataset @xcite . experimental results demonstrate that our proposed method outperforms strong baselines based on bert @xcite in two-step settings, and achieves competitive results compared to those of the state-of-the-art supervised end-to-end methods. moreover, we apply the disentangled conversations predicted by our method to the downstream task of multi-party response selection and get significant improvements compared to a baseline system. 3 in summary, our main contributions are three-fold: dialogue structure learning one problem that may be related to conversation disentanglement is dialogue structure learning @xcite . both are related to understanding multi-party conversation structures but they are different tasks. dialogue structure learning aims to discover latent dialogue topics and construct an implicit utterance dependency tree to represent a multi-party dialogue's turn taking @xcite , while the goal of conversation disentanglement is to learn an explicit dividing scheme that separates intermingled messages into sessions. co-training co-training @xcite has been widely used as a low-resource learning algorithm in natural language processing @xcite , which assumes that the data has two complementary views and utilizes two models to iteratively provide pseudo training signals to each other. our method consists of a message-pair classifier and a session classifier, which respectively view the unannotated dataset from the perspective of the local relations between two messages and that of the context-aware relations between a session and a message. to the best of our knowledge, this is the first work that utilizes co-training in the research of conversation disentanglement. we will extend the co-training idea to the deep learning paradigm to construct novel models for disentanglement. ## formulation and notations and k are unknown to the model. our goal is to learn a dividing scheme that indicates which session a message m i belongs to. we solve this task in an end-to-end fashion where we formulate unsupervised conversation disentanglement as an unsupervised sequence labeling task. for a given message m i , there exists a session set @xmath0, • • • , t z(i) } where z(i) indicates the number of detached sessions when m i is being processed. the model needs to decide if m i belongs to any session in t. if m @xmath1, then m i will be appended to t k ; otherwise a new session t z(i)+1 will be built and initialized by m i , and the new session will be added to t. ## method in this section, we describe our co-training based framework in detail, which contains following components: ## conclusion this is the first work to investigate unsupervised conversation disentanglement with deep neural models. we propose a novel approach based on cotraining which consists of a message-pair classifier and a session classifier. by iteratively updating the two classifiers with the help of each other, the proposed model attains a performance comparable to that of the state-of-the-art supervised disentanglement methods. experiments on downstream tasks proves that our method can help better understand multi-party conversations. our method can be easily adapted to a different assumption, and also it can be extended to other low-resourced scenarios like semi-supervised settings, which we will leave as our future work.