A Deeper (Autoregressive) Approach to Non-Convergent Discourse Parsing (2023)

Abstract: Online social platforms provide a bustling arena for information-sharing and for multi-party discussions. Various frameworks for dialogic discourse parsing were developed and used for the processing of discussions and for predicting the productivity of a dialogue. However, most of these frameworks are not suitable for the analysis of contentious discussions that are commonplace in many online platforms. A novel multi-label scheme for contentious dialog parsing was recently introduced by Zakharov et al. (2021). While the schema is well developed, the computational approach they provide is both naive and inefficient, as a different model (architecture) using a different representation of the input, is trained for each of the 31 tags in the annotation scheme. Moreover, all their models assume full knowledge of label collocations and context, which is unlikely in any realistic setting. In this work, we present a unified model for Non-Convergent Discourse Parsing that does not require any additional input other than the previous dialog utterances. We fine-tuned a RoBERTa backbone, combining embeddings of the utterance, the context and the labels through GRN layers and an asymmetric loss function. Overall, our model achieves results comparable with SOTA, without using label collocation and without training a unique architecture/model for each label. Our proposed architecture makes the labeling feasible at large scale, promoting the development of tools that deepen our understanding of discourse dynamics.

## introduction online discourse has become a major part of modern communication due to the proliferation of online social platforms that allow people to easily share their ideas with a global audience. however, the ease of communication has also led to more heated debates and arguments that sometimes devolve into personal attacks @xcite @xcite , and increase political and societal polarization @xcite . the ability to parse contentious discussions at a large scale bears practical and theoretical benefits. from a theoretical perspective it would allow the research community at large (social scientists and computational scientists alike) to better track and understand conversational and societal dynamics. from a practical perspective, it was found that early intervention by a human moderator or facilitator can improve the productivity and focus of a discussion @xcite . discourse parsing can be the first step in developing assistive moderation tools that can be employed at scale and promote a more productive discourse. it is commonly argued that the convergence of views indicates the success (or productiveness) of a conversation @xcite @xcite . this perspective has been reflected in discourse annotation schemes that were proposed through the years @xcite . however, the equation of productiveness with convergence is being challenged based on both theoretical and empirical grounds, as non-convergent discussions can be very productive, as they serve as a fruitful venue for the development of dialogic agency @xcite @xcite @xcite . the non-convergence perspective inspired a novel annotation scheme that was recently introduced by @xcite . its organizing principle is responsiveness, rather than acceptance and convergence of ideas -a productive discussion is one in which the interlocutors use speech acts that exhibit high responsiveness, while acts of low responsiveness deem the discussion unproductive. it is important to note that responsiveness is not the mere act of producing a response, but the act of responding in good faith. the application of this schema is illustrated by the two snippets in figure 1 . in the short exchange in figure 1a , the first speaker uses sarcasm foot_0 , and later responds aggressively to a counter argument. the dialogue then goes from bad to worse with a series of direct no utterances. the other discussion (figure 1b ) demonstrates how counter argument and critical question push for a reasoned answer, even though the topic is highly divisive. another interesting observation that applies to many online discussions is the way argumentation tends to introduce sub-topics as rhetoric devices foot_1 . subscribing to this annotation scheme, the conversational discourse parsing (cdp) task can be viewed as a sequence-of-utterances to sequence-ofsets task: an utterance can be labeled by multiple labels concurrently. for clarity, we provide a brief explanation of the tagset in section 3. a formal definition of the computational task is presented in section 4.1. the need for a dedicated discourse schema and the development of the tagset were well motivated by @xcite . the authors released an annotated dataset of ∼ 10k utterances and demonstrated the feasibility of learning the annotation task. however, their computational approach suffers from a number of drawbacks: first, they cast the prediction task as a binary classification and trained a model for each tag separately. second, considering the prediction of tag l ′ to an utterance u i , they assumed access to an oracle providing complete and accurate knowledge of gold labels of preceding utterances and the correct binary assignment of all other tags for u i . this very strong assumption is not realistic in any real-world scenario. finally, the results they report were achieved after feature engineering and an extensive grid search on the classifier and the features space. consequently, each tag is predicted using a different classification framework, based on a uniquely crafted feature set. in this work, we present n-codip -a unified autoregressive transformer for non-convergent discourse parsing. the model is trained to predict all labels together without using any external knowledge provided by an oracle. n-codip performance (f-score macro and weighted averages) is compara-ble with the best results reported by @xcite without suffering from any its drawbacks. our proposed model uses the roberta architecture @xcite as the backbone. we use simcse @xcite for sentence embedding and feed preceding utterances through a gated residual network (grn) @xcite . the model is fine-tuned using an asymmetric loss function that was recently demonstrated to improve performance in imbalanced multi-label assignment in vision @xcite . to the best of our knowledge, this is the first application of this loss function in this domain. we provide a detailed description of the architecture in section 4. results and analysis are provided in section 6. ## related work conversational discourse parsing there have been numerous dialog corpora collected and labeled with various schemes to model discourse structure. @xcite presented the switchboard-damsl dialog act schema on a dataset of cooperative, task-oriented dialogues between pairs of interlocutors in phone conversations @xcite . this was extended in @xcite to allow for more thorough analysis of linguistic features in dialog. there have been multiple studies approaching the dialog act classification problem with deep neural networks including transformers, with some emphasizing the importance of integrating context information from previous utterances @xcite @xcite . the switchboard-damsl corpus is a two party discourse analysis schema, which is different from the multi-party discourse parsing schema presented in @xcite and modeled in this work. multi-party dialog corpora such as stac @xcite as well as the ubuntu unlabeled corpus @xcite and its labeled extension the molweni discourse relation dataset @xcite are more closely related to the current task, though the discourse is not contentious and the utterances tend to be quite short when compared to messages in the cmv forum debates. another key difference between these and the cdp corpus is that in the latter, the label scheme is oriented towards a more basic understanding of the components of a productive discourse, while the former is more focused on characterizing basic dialog acts. cmv and discourse owing to the high quality of its discussions, cmv discussions are commonly used as a data source for various nlp and social science research, ranging from argument mining to the study of the effects of forum norms and moderation, as well as persuasive text analysis and linguistic style accommodation, e.g., @xcite al. (2016); khazaei et al. (2017); musi et al. (2018); jo et al. (2018); xiao and khazaei (2019) ; @xcite ; @xcite . argumentation and argument mining argument mining is another related line of research, for a comprehensive survey see @xcite . argument mining is done on long-form documents, e.g., wikipedia pages and scientific papers @xcite or in dialogical contexts, e.g., twitter, wikipedia discussion pages, and reddit-cmv @xcite @xcite . argument mining enables a nuanced classification of utterances into discourse acts: socializing, providing evidence, enhancing understanding, act recommendation, question, conclusion, and so forth @xcite . most of the argument mining work is aimed at identifying stance and opinionated utterance or generating arguments or supportive evidence to end users conducting formal debates @xcite . our work is inspired by these works, although our focus is on the way discursive acts reflect and promote responsiveness, rather than simply labeling texts as bearing 'evidence' or posing a 'question'. moreover, while our focus is contentious non-convergent discussions, we wish to characterize discussions as win-win, rather than a competition. ## experimental settings baselines we compare our n-codip architecture to previously reported results in @xcite . we focus on two sets of reported results: ## conclusion and future work theoretical framework and empirical evidence motivates the need for a discourse annotation schema that reflects discursive moves in contentious discussions. we introduced n-codip, a unified non-convergent-discussion parser that outperforms previous work in a discourse parsing task based on the scheme that was recently developed and shared by @xcite . we have demonstrated that using grn layers, previously used for multi-horizon time-series forecasting by @xcite and an asymmetric loss function, previously used in computer vision by @xcite is especially beneficial to the task at hand, given the relatively small dataset, the imbalanced tagset, and the multi-label setting. future work will take theoretical and computational trajectories. a robust error analysis will be done with respect to the theoretical framework behind the annotation scheme. computationally, we will investigate better ways to better leverage the abundance of structured unlabeled data (thousands of discussion on cmv and other platforms) as an auxiliary task, and achieve a better integration of the context turn-taking structure with the model.