Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure (2024)

Abstract: Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.

## introduction the growing popularity of online platforms have resulted in the revolution of interpersonal communications. individuals now engage in diverse forms of online conversations to exchange viewpoints and share ideas. it allows users to access an abundance of fresh materials, whereas the explosive growth of online texts -essentially conversational and usually in multiple threads @xcite -has also hindered human capability to find the information needed. there consequently presents a pressing need to develop conversation understanding methods to automatically digest massive texts and complex interactions therein. to that end, it is crucial to capture the interactions of who responded to whom -the base to build and understand the conversation structure, as pointed out in many previous studies @xcite [c1] i am aware that you can thank them in private argument but what does that matter? figure 1 : a reddit conversation snippet. c 1 and r is an initiation-response pair while c 2 to c 5 are the other four candidates. topic words reflecting the discussion points "public gratitude expression" are in bold. the blue and italic "that" occurring in both c 1 and r imply r's possible intention to answer c 1 's question. @xcite . by reflecting how participants interact with each other, such structure has shown useful to predict users' online social activities @xcite , summarize key discussion topics @xcite , measure argument persuasiveness @xcite , and so forth. to date, despite of the extensive efforts on user interaction modeling, many of them employ userannotated in-reply-to signals, such as @-mention on twitter @xcite . nonetheless, such labels are usually unavailable or unreliable @xcite , especially for online conversations in informal styles. other studies assume utterances only respond to their chronological neighbors @xcite , largely ignoring the longdistance interactions prominent in online conversations @xcite . all these concerns lay down our objective to investigate who responded to whom in conversation contexts. following previous practice @xcite , we define our task to predict pairwise initiation utterances and their responses in an online con-versation (henceforth initiation-response pairs), where an initiation sets up an expectation earlier and its response later react to it in process of a discussion. to illustrate our task, figure 1 shows an example response r and the other five utterances c 1 to c 5 from r's previous post in a reddit conversation. our goal is to identify which utterance from c 1 to c 5 is r's initiation. as can be seen, r is most likely to respond to c 1 for two possible reasons: first, they both focus on the topic of public gratitude expression (as topic words "thank", "public", "gratitude" are mentioned); second, c 1 raises a question (signaled by "what" and the question mark "?") that can be well answered by r (via echoing the pronoun "that"). here, we examine two latent factors that implicitly link an initiation and its response -the consistency of the topics they center around (henceforth topic consistency) and the dependency of their discourse roles (henceforth discourse dependency). our intuition is that responses tend to follow the points pushed forward in their initiations (such as public gratitude expression in figure 1 ) and their discourse roles are likely to exhibit some dependency in interactions, such as an answer responding to an initiated question (like r answering c 1 in figure 1 ) and an argument followed by another argument in a back-and-forth debate. to the best of our knowledge, we are the first to analyze the effects of topics and discourse in conversational responding behavior, while previous work predict initiation-response pairs without modeling such latent factors embedded in the relations @xcite . to learn topics and discourse, we separate two word distributions for representing each of them. the latent variables are inferred with a neural architecture in an unsupervised manner @xcite , which enables topic and discourse inference without either manually annotated data @xcite or expertise involvements to customize model inference @xcite . afterwards, two neural modules are employed, one to capture topic consistency and the other discourse dependency, both aim to explore the implicit links of a response and a candidate initiation. the learned representations are hence coupled to predict how likely the two utterances form an initiation-response pair. in an empirical study, we carry out extensive experiments on two conversation datasets, one contains english argumentative discussions on reddit (from the changemyview subreddit), and the other chinese customer service dialogues from e-commerce platform wangwang. both of them will be released upon publication as part of our work. the experimental results show that our model significantly outperforms state-of-the-art methods from previous work. for example, we achieve 79.02 mrr on the wangwang dialogues compared with 72.69 produced by @xcite . in extensive analyses on latent topics and discourse, we find that meaningful representations can be learned by our model and both topics and discourse may contribute to indicate initiation-response pairs. lastly, we show that our learned representation to indicate initiation-response relations can further benefit to identify persuasive arguments in social media debates. ## learning topics and discourse effects for initiation-response prediction the overall architecture of our model is shown in figure 4 (a). it takes an initiation candidate q, a response r, and their corresponding contexts c q and c r as inputs. the outputs are matching scores indicating how likely r responds to q. ## experimental setup data preprocessing. for cmv dataset, the raw data was preprocessed by @xcite . we first filter out tokens occurring less than 15 to alleviate sparsity and maintain a vocabulary with 15, 182 tokens. then, we remove too short (with less than 7 words) and too long utterances (with over 45 words) to better explore utterance-level word statistics for topic and discourse modeling. next, to form context for quotations and replies (c q and c r ), we consider all utterances in the original post (from oh) as c q and those in the challenger's comment as c r . lastly, the training and test data is separated following @xcite , where 6, 839 pairs are used for training and and 1, 098 for test. for cs dataset, we don't remove words and the vocabulary size is 15, 407, with the scale similar to cmv. short utterances with less than 5 words are removed. the chinese word segmentation and the separation of training and test set has been done by @xcite , with 3, 701 and 576 instances for training and test. here all utterances in the dialogue thread are used to form both c q and c r due to the synchronous nature of cs conversations. for both datasets, 10% data is further sampled from the training set for validation. ## related work our work is in the line with prior efforts to detect initiation-response pairs. wang and rosé (2010) explore how topic features discovered via latent semantic analysis (lsa) work in this task, largely ignoring the effects of discourse roles. on the contrary, our study shows that both topics and discourse are helpful to identify who respond to whom in conversation structure. other related work @xcite @xcite focus on the design of hand-crafted features. recently, there exists a growing attention over how neural framework perform to identify replying relations in conversation discourse @xcite . however, they ignore the effects of latent topics and discourse to structure a conversation, which are extensively studied here and shown useful to indicate initiation-response relations in experiments. we are also inspired by the previous approaches to discover latent topics and discourse in conversations contexts. many of them employ probabilistic graphical models in lda-fashion to explore word statistics @xcite @xcite . we take the advantage of the recent progress to explore conversation representations via variational autoencoders (vae) @xcite @xcite , allowing to capture topic and discourse factors in an unsupervised manner. however, their effects to signal user interactions in conversation structure have never been studied before, which is a gap our work fills in. ## conclusion this work explores the effects of latent topics and discourse roles to signal initiation-response relations that structure a conversation. we first employ a vae-based neural model to capture topic and discourse representations in an unsupervised manner. then, topic consistency and discourse dependency are further exploited to predict how likely an utterance responds to an initiation. extensive experiments on large-scale datasets containing asynchronous english argumentative conversations (from the cmv subreddit) and synchronous chinese customer service dialogues (from wangwang platform) show that our model significantly outperform the previous state-of-the-art models.