A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing (2022)

Abstract: Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation. Existing works are generally divided into two lines: graph-based and transition-based paradigms, which perform well for short-distance and long-distance dependency links, respectively. However, there is no study to consider the advantages of both paradigms to facilitate conversational discourse parsing. As a result, we propose a distance-aware multi-task framework DAMT that incorporates the strengths of transition-based paradigm to facilitate the graph-based paradigm from the encoding and decoding process. To promote multi-task learning on two paradigms, we first introduce an Encoding Interactive Module (EIM) to enhance the flow of semantic information between both two paradigms during the encoding step. And then we apply a Distance-Aware Graph Convolutional Network (DAGCN) in the decoding process, which can incorporate the different-distance dependency links predicted by the transition-based paradigm to facilitate the decoding of the graph-based paradigm. The experimental results on the datasets STAC and Molweni show that our method can significantly improve the performance of the SOTA graph-based paradigm on long-distance dependency links.

## introduction the goal of conversational discourse parsing is to uncover latent conversation topics and construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation. since the discourse structure is essential to understand multiparty conversations, it has been widely applied to various natural language processing (nlp) applications,such as response generation @xcite (u 1 ) a: headers , kernel image and all the rest (u 2 ) b: what project are you trying to build ? (u 3 ) a: this did n't work either , i guess that the k cc is correctly registered but fails . (u 4 ) c: his gcc segfaults on helloworld (u 5 ) b: ahh , so , a libc issue most likelyis there a kk pastebin url of the errors ? (u 6 ) d: are you using any backports or sth ? (u 7 ) b: can you pastebin the results of that `` gcc kk -v test.c '' if there are errors ? (u 8 ) b: do you have the matching kernel-headers kk and glibc-devel packages installed ? (u 9 ) a: can you help me how to find that out ? segmented discourse representation theory (sdrt) @xcite is one of the most influential theories to reveal the overall discourse structures in conversational discourse parsing. unlike rhetorical structure theory (rst) @xcite , which limits the relationship to occur between adjacent edus foot_0 in monologue, sdrt represents multi-party conversations as dependency-based discourse structures, due to crossing dependencies. recently, some sdrt-style corpora have been built, such as stac @xcite and molweni @xcite . figure 1 shows an example of a multi-party conversation and its dependency structure from molweni. existing work on conversational discourse parsing can be divided into two lines: graph-based and transition-based paradigms. the graph-based paradigm @xcite @xcite @xcite first obtains the probability of the discourse relation for each edu pair, then a global decoding method is applied to construct the discourse structure. the transition-based paradigm @xcite first obtains the probability of the discourse relation between the current edu and all previous edus, then discourse structure is constructed incrementally. due to the discrepancy in the parsing process, both of them have different strengths in predicting the dependency links at various distances. as shown in figure 2 , we analysis the performance of dependency links at different distances predicted by the above two paradigms. the results show that the graph-based paradigm performs better for dependency links with the distance 1, while the transition-based paradigms performs better when the distance greater than 1. as a result, it is a great challenge to combine the advantages of both two paradigms to facilitate conversational discourse parsing. previous work @xcite has demonstrated the effectiveness of multi-task learning to integrate both two paradigms in a similar task dependency syntactic parsing. through the shared encoding layer, both two paradigms can facilitate each other implicitly. however, it is not sufficient to apply this approach to conversational discourse parsing, because the advantages of one paradigm cannot be explicitly exploited to facilitate the other one. to alleviate the above issues, we propose a distance-aware multi-task framework (damt) for conversational discourse parsing that allows one paradigm to explicitly facilitate the decoding process of the other from the encoding and decoding process, respectively. specially, we introduce an encoding interactive module (eim) to enhance the flow of semantic information between both two paradigms during the encoding step. and then we apply a distance-aware graph convolutional network (dagcn) in the decoding process, which can incorporate the different-distance dependency links predicted by the transition-based paradigm to facilitate the decoding of the graph-based paradigm. the experimental results on two datasets stac and molweni show that our damt outperforms the sota baselines, especially the significant improvement on those dependency links with long distances. ## related work most previous studies for overall discourse structure parsing are based on rhetorical structure theory discourse treebank (rst-dt) @xcite , including greedy bottom-up approach @xcite , cyk-based approaches @xcite and transitionbased methods @xcite @xcite . in this paper, we focus on parsing conversational dependency structures that allow crossing dependencies. recently, there are two available corpora, i.e., stac @xcite and molweni @xcite ) defined 16 relation types. stac collected from an online game the settlers of catan, which contains 1,062 and 111 dialogues for training and testing, respectively. molweni is based on ubuntu chat @xcite , which contains 9,000, 500 and 500 instances for training, validating and testing, respectively. up to now, only a few studies focused on con-结构 (1,2),(1,3),(1,4),(1,5),(5,6),(6,7) versational discourse parsing and most of them can be divided into two paradigms, i.e., graph-based paradigm and transition-based paradigm. graph-based paradigm muller et al. (2012), afantenos et al. (2015) and perret et al. (2016) adopted traditional manual features to calculate the probabilities of all edu pairs and then global decoding algorithm (e.g., maximum spanning trees, a* and integer linear programming) was used to construct dependency structures. with the development of deep learning, some advanced methods are used to obtain the semantic representation of each edu pair. wang et al. (2021a) proposed a novel edge-centric graph neural network to enhance the semantic representation of edus. yang et al. (2021) first used the dependency syntactic graph to obtain a better edu representation and then a biaffine relation prediction layer was applied to obtain the probability of each edu pair. transition-based paradigm shi and huang (2019) proposed a deep sequence model (dsm) to predict dependency links and corresponding relation types jointly and alternately. their model not only consider the local information of the concerned edus but also utilizes the historical structure. based on dsm, wang et al. (2021b) adopted the graph attention network by incorporating cohesion information including lexical chain and coreference chain to enhance the semantic representation of edus. ## damt our framwwork damt is shown in figure 3 , which includes four components: encoding mod-ule (em), encoding interaction module (eim), transition-based decoding module (tdm), and distance-aware decoding module (dadm). in em, the hierarchical gru is applied to obtain the semantic representation of dialogues. in eim, the semantic representations of different paradigms can interact explicitly to promote the multi-task learning. in tdm, we adopt a pointer network for transition-based decoding to obtain the dependency structures. in dadm, the dependency structures predicted by tdm are incorporated by dagcn for the final graph-based decoding. ## analysis in this section we first conduct a detailed analysis on the dependency links with different distances and the different lengths of dialogues, and then provide the ablation study and case study. ## conclusion in this paper, we propose a distance-aware multitask framework damt to facilitate conversational discourse parsing. first, we propose an encoding interaction module to enhance the information flow between the graph-based paradigm and transitionbased paradigm to promote multi-task learning. second, we propose a distance-aware graph convolutional network dagcn incorporating the dependency structure predicted by one paradigm to explicitly facilitate the decoding process of another paradigm. the experimental results on two public datasets show the effectiveness of our proposed damt. in the future, we will further explore how to recognize relation types more effectively.