Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection (2021)

Abstract: Dialogue disentanglement aims to group utterances in a long and multi-participant dialogue into threads. This is useful for discourse analysis and downstream applications such as dialogue response selection, where it can be the first step to construct a clean context/response set. Unfortunately, labeling all reply-to links takes quadratic effort w.r.t the number of utterances: an annotator must check all preceding utterances to identify the one to which the current utterance is a reply. In this paper, we are the first to propose a zero-shot dialogue disentanglement solution. Firstly, we train a model on a multi-participant response selection dataset harvested from the web which is not annotated; we then apply the trained model to perform zero-shot dialogue disentanglement. Without any labeled data, our model can achieve a cluster F1 score of 25. We also fine-tune the model using various amounts of labeled data. Experiments show that with only 10% of the data, we achieve nearly the same performance of using the full dataset.

## introduction multi-participant chat platforms such as messenger and whatsapp are common on the internet. while being easy to communicate with others, messages often flood into a single channel, entangling chat history which is poorly organized and difficult to structure. in contrast, slack provides a threadopening feature that allows users to manually organize their discussions. it would be ideal if we could design an algorithm to automatically organize an entangled conversation into its constituent threads. this is referred to as the task of dialogue disentanglement @xcite @xcite @xcite @xcite @xcite ). 1 code is released at https://github . training data for the dialogue disentanglement task is difficult to acquire due to the need for manual annotation. typically, the data is annotated in the reply-to links format, i.e. every utterance is linked to one preceding utterance. the effort is quadratic w.r.t the length of dialogue, partly explaining the sole existence of humanannotated large-scale dataset @xcite , which was constructed based on the ubuntu irc forum. to circumvent the need for expensive labeled data, we aim to train a self-supervised model first then use the model to perform zero-shot dialogue disentanglement. in other words, our goal is to find a task that can learn implicit reply-to links without labeled data. ## conclusion in this paper, we first demonstrate that entangled response selection does not require complex heuristics for context pruning. this implies the model might have learned implicit reply-to links useful for dialogue disentanglement. by introducing intrinsic attention supervision to shape the distribution, our proposed method can perform zero-shot dialogue disentanglement. finally, with only 10% of the data for tuning, our model can achieve 92% of the performance of the model trained on full labeled data. our method is the first attempt to zero-shot dialogue disentanglement, and it can be of high practical value for real-world applications.