Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023)

Abstract: Dialogue discourse parsing aims to reflect the relation-based structure of dialogue by establishing discourse links according to discourse relations. To alleviate data sparsity, previous studies have adopted multitasking approaches to jointly learn dialogue discourse parsing with related tasks (e.g., reading comprehension) that require additional human annotation, thus limiting their generality. In this paper, we propose a multitasking framework that integrates dialogue discourse parsing with its neighboring task addressee recognition. Addressee recognition reveals the reply-to structure that partially overlaps with the relation-based structure, which can be exploited to facilitate relation-based structure learning. To this end, we first proposed a reinforcement learning agent to identify training examples from addressee recognition that are most helpful for dialog discourse parsing. Then, a task-aware structure transformer is designed to capture the shared and private dialogue structure of different tasks, thereby further promoting dialogue discourse parsing. Experimental results on both the Molweni and STAC datasets show that our proposed method can outperform the SOTA baselines. The code will be available at https://github.com/yxfanSuda/RLTST.

## introduction dialogue discourse parsing aims to construct a dependency tree on discourse relations to reflect the implicit discourse structure of a dialogue, which is helpful for various downstream tasks, such as reading comprehension @xcite , meeting summarization @xcite , response generation @xcite and sentiment analysis @xcite . most studies on discourse parsing usually focus on monologue and represent a document with a hierarchical tree on the rhetorical structure theory @xcite , where cont, qap, ack, and com refer to continuation, question-answer_pair, acknowledgement and comment, respectively. we also present the reply-to structure of addressee recognition (left lines) in this example for better illustration. dialogue discourse parsing reveals the relation-based structure by establishing discourse links on discourse relations, while addressee recognition reveals the reply-to structure by identifying the addressee for each utterance. the black-solid lines indicate the shared structure between the two tasks and the orange-dot lines and the blue-dashed lines indicate the private structure of addressee recognition and dialogue discourse parsing, respectively. dialogue discourse parsing suffers from data sparsity because annotating the links and relations requires a precise understanding of dialogues, which is time-consuming and expensive. to mitigate the problem of data sparsity, previous works explore the complementary knowledge of related tasks to facilitate dialogue discourse parsing, including joint training with reading comprehension @xcite or dropped pronoun recovery @xcite . however, the requirements of additional human annotation, such as the questions and answers for reading comprehension, limit the generality. in this paper, we jointly learn dialogue discourse parsing with its neighbor task addressee recognition @xcite @xcite to alleviate the issue of data sparsity and additional human annotation requirements. addressee recognition reflects the reply-to structure between different speakers, while dialogue discourse parsing reveals the relation-based structure between the same or different speakers. therefore, the two tasks have both shared and private structures as shown in figure 1 . our intuition is to leverage shared structures of addressee recognition to promote dialogue discourse parsing in a multitasking manner. however, two challenges remain when jointly training our task with addressee recognition. the first challenge arises from the fact that examples from addressee recognition are not equally beneficial for discourse parsing due to the partially overlapping structure between the two tasks. it becomes crucial to identify the most useful examples from the data-rich addressee recognition to improve multitask learning. the second challenge involves how to capture shared and private structures of different tasks. as shown in figure 1 , two tasks have both their shared structure (black-solid lines) and their private structure (orange-dot and blue-dashed lines). thus, capturing the shared and private structure poses an additional challenge in effectively promoting dialogue discourse parsing. to tackle the first challenge, we introduce a reinforcement-learning agent to identify the training examples from addressee recognition that contribute the most to dialogue discourse parsing. to address the second challenge, we propose a taskaware structure transformer that effectively exploits the shared and private dialogue structures across different tasks. our approach involves evaluating the importance of each neuron in the structure transformer and categorizing them as either task-shared or task-private based on their significance for each task. subsequently, the task-shared neurons are updated by all tasks, while the task-private neurons are updated by each specific task individually. experimental results on both molweni and stac show that our proposed method outperforms the sota baselines. besides, the reinforcementlearning agent can reduce about 80% of the training data of addressee recognition and help dialogue discourse parsing achieve higher performance. in addition, the task-aware structure transformer can effectively capture shared and private structures of different tasks, thus further promoting dialogue discourse parsing. ## approach as shown in figure 2 , our method mainly includes two steps: data selection on reinforcement learning and multitask learning on task-aware structure transformer (tst). in the following sections, we first introduce the backbone of our method, followed by the reinforcement-learning agent and taskaware structure transformer. ## conclusion in this paper, we proposed to improve dialogue discourse parsing by leveraging reply-to structures of addressee recognition in a multitasking manner. to this end, we first adopt a reinforcement-learning agent to identify training examples from addressee recognition that help dialogue discourse parsing the most. then, we proposed a task-aware structure transformer to capture the shared and private structures of different tasks. experimental results on molweni and stac verify the effectiveness of our method. in the future, we will focus on how to incorporate multiple relevant tasks to improve dialogue discourse parsing.