Unsupervised Learning of Hierarchical Conversation Structure (2022)

Abstract: Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure, but it can be highly domain-dependent. This work introduces an unsupervised approach to learning hierarchical conversation structure, including turn and sub-dialogue segment labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The decoded structure is shown to be useful in enhancing neural models of language for three conversation-level understanding tasks. Further, the learned finite-state sub-dialogue network is made interpretable through automatic summarization.

## introduction increasingly, language understanding applications involve conversational speech and text. much attention has recently been directed at human-agent dialogue systems, including virtual assistants, interactive problem solving, and information seeking tasks (e.g., conversational question answering). however, automatic understanding of human-human conversations is also of interest for problems such as call-center analytics, conversation outcome prediction, meeting summarization, and human-agent interaction involving multiple people. the focus of this paper is on human-human conversation understanding. like written documents, goal-oriented conversations tend to have structure (openings, context setting, problem solving, etc.). however, in humanhuman conversations (both text and speech), participant roles factor into the structure, and the structure is less rigid due to the need to accommodate miscommunications and varying objectives. yet, most work on conversational systems treats dialogues like written text, i.e., the dialogue history is we release our code for experiments at https:// github.com/boru-roylu/theta . a linear sequence of text. in this paper, we explore unsupervised learning strategies for adding structural information to a state-of-the-art hierarchical transformer-based model of text. linguistic analysis of conversations often involves associating speaker utterances with dialogue acts (das), e.g., question, statement, backchannel, clarification, etc. @xcite , and segmenting the conversation into nested subsequences of participant turns that reflect a common topic or conversational goal @xcite . past studies have explored using such structure, particularly das, to improve automated human-agent dialogues. here, we use hierarchical structure (both turn-level da labels and sub-dialogue states) to improve classification of human-human conversations. specifically, we introduce three-stream hierarchical transformer (theta), which integrates transformer representations of the da and sub-dialogue state sequences into a hierarchical transformer (ht) @xcite operating on the original text. in addition to improving performance, the use of discrete structural cues in classification can support conversation analysis. for example, we can identify seller strategies that are more likely to lead to a successful outcome or use the subdialogue state sequence to summarize frequently visited states in unsuccessful interactions. since hand-annotation of structure can be costly and inventories vary across tasks, there is substantial interest in unsupervised learning of structure for specific task domains. here, the approach to structure learning involves two steps. first, we use a clustering algorithm to learn a mapping of utterance embeddings to discrete categories, which serve as an unsupervised version of das. each conversation is then represented by the discrete sequence of cluster identifiers (ids) associated with the sequence of utterances. using the collection of discretized conversations, we automatically learn the topology of a latent finite-state model over these sequences, i.e., a hidden markov model (hmm), using a greedy state-splitting algorithm that maximizes the likelihood of the sequence data without requiring any annotations. the states of the hmm correspond to different sub-dialogues that may be associated with specific topics, strategies or subtasks. the sub-dialogue structure of a new conversation is identified by finding the most likely state sequence given that discretized utterance sequence. the learned structure is assessed in experiments on three conversation-level classification tasks: buyer/seller negotiation outcomes on craigslist-bargain @xcite , conversation category in the action-based conversations dataset (abcd) @xcite , and client callback prediction in a private call center corpus. in each task, we find that a combination of both utterance-level category and sub-dialogue state information lead to improved performance. further, we use automatically generated descriptions of the clusters and subdialogue states to provide an interpretable view of the finite-state topology and a summarized view of a conversation. anecdotally, we find that this structure lends insights into how participant strategies (state paths) are associated with different conversation outcomes. the contributions of this work are as follows. first, we introduce a simple unsupervised approach to learn a hierarchical representation of conversation structure that includes turn-level labels and sub-dialogue segmentation, accounting for participant role. using three conversation-level classification tasks, we demonstrate that integrating the structural information into a state-of-the-art hierarchical transformer consistently improves performance. lastly, we show how the discrete representation of structure combined with automatic summarization can provide a mechanism for interpreting what the model is learning or for conversation summarization and analytics. ## method as shown in figure 1 , theta represents the sequence of turns in a conversation using: i) a hierarchical transformer (ht) operating on a turnsegmented word sequence, ii) a transformer operating on a sequence of turn-level das, and iii) a separate transformer operating on a sequence of sub-dialogue states derived from the das. the conversation-level vectors produced by the three transformers are concatenated and used in a final task-specific layer for conversation classification tasks. the ht alone is the state-of-the-art model for conversation-level tasks. the da and sub-dialogue states comprise the structural information that enhances the ht for improving performance of the end task. in addition, the discrete nature of the structure representation provides a mechanism for analyzing the conversation classes via summarization of utterances associated with the da labels or sub-dialogue states. ## interpretation and analysis in this section, we leverage automatic summarization of clusters and states to derive insights into the learned conversation structure, both for interpretability of the model and for applications such as conversation analytics and summarization. as an example, we analyze fine-grained components from the learned topology, i.e., most frequent paths and individual state n-grams, to investigate their associations with different dialogue characteristics. we apply graph-based unsupervised summarization @xcite over utterances in each state (decoupling participant roles) and in each cluster. on craigslist-bargain and abcd, this leads to more than 3× reduction in conversation length. figure 3 shows the 8-state topology of craigslistbargain with selected state summaries. based on the summaries, it is easy to see that s1 and s8 capture opening and closing das, respectively, while s5 and s6 correspond to different negotiation strategies. we also find that conversations with shorter paths are likely to involve a less experienced seller or lower buyer interest, e.g., 92% conversations with path s1-s2-s8 lead to under listing sells. on the other hand, sellers that say offers are too low are more likely get better prices, e.g., 91% conversations with path s1-s2-s3-s5-s7-s8. abcd ## related work hmms have been leveraged for learning structure in language for many years, such as in early work on inducing word-level part-of-speech tags @xcite . accordingly, most work on unsupervised learning of both das and conversation structure leverages hmms. ## conclusion in summary, this work combines two simple approaches for unsupervised learning on top of embedded utterance representations (k-means clustering and hmm topology design) to derive a hierarchical representation of conversation structure, which is useful to enhance a hierarchical trans-former in three conversation-level classification tasks. the k-means clusters are intended to approximate das and the hmm is intended to learn subdialogue structure. unlike prior work in this area, the sub-dialogues build on da sequences rather than unigram/bigram statistics, and the hmm incorporates forward-moving dialogue flow constraints in topology learning, with the goal of capturing sub-dialogue function.