DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement

戏剧性对话解缠

Dramatic Conversation Disentanglement

本文构建了基于电影和电视剧的对话解缠数据集，借鉴社会语言学理论定义会话线程并标注了831部电影的万余对话轮次，通过模型对比与大规模应用揭示了戏剧对话中线程长度和性别与话题发起的关系。

A Large-Scale Corpus for Conversation Disentanglement

Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 80% of conversations in a …

Unsupervised Conversation Disentanglement through Co-Training

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 are expensive to obtain in practice. In this work, we explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible for retrieving …

观察说话者：一种用于对话中情绪识别且具备情绪解耦的混合连续归因网络

Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement

本文提出混合连续归因网络HCAN，通过建模情绪连续性与情绪归因，并引入情绪认知损失缓解说话者建模过拟合，在多个数据集上取得领先性能。

Online Conversation Disentanglement with Pointer Networks

Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only following the ongoing discussions but also extracting relevant information from simultaneous messages. Conversation disentanglement aims to separate intermingled messages into detached conversations. However, existing disentanglement methods rely mostly on handcrafted features that are dataset specific, which hinders generalization and adaptability. In this work, we propose an end-to-end online framework for conversation …

Findings on Conversation Disentanglement

Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and conversation summarization. Framing it as a utterance-to-utterance classification problem -- i.e. given an utterance of interest (UOI), find which past utterance it replies to -- we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utteranc…