Graph Based Network with Contextualized Representations of Turns in Dialogue (2021)

Abstract: Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN .

## introduction the task of relation extraction (re) aims to identify semantic relations between arguments from a text, such as a sentence, a document, or even a dialogue. however, since a large number of relational facts are expressed in multiple sentences, sentence-level re suffers from inevitable restrictions in practice @xcite . therefore, cross-sentence re, which aims to identify relations between two arguments that are not mentioned in the same sentence or relations that cannot be supported by any single sentence, is an essential step in building knowledge bases from large-scale corpora automatically @xcite @xcite . in this respect, because dialogues to support the prediction of relation(s) between two arguments that appear within a dialogue, @xcite recently proposed dialogre, which is a human-annotated dialogue-based re dataset. there are several major challenges in effective relation extraction from dialogue, inspired by the way how people understand dialogue in practice. first, the dialogue has speakers, and who speaks each utterance matters. the reason for it is because the subject and object of rela-tional triples depend on who is speaking which utterance. for example, if s3 answered "hey!" after "hey pheebs.", the relational triple (s2, per:alternate_names, pheebs) will be revised to (s3, per:alternate_names, pheebs), in the case of in this paper, we propose the turn context aware graph convolutional network (tucore-gcn) for dialogue-based re. it is designed to tackle the aforementioned challenges. tucore-gcn encodes the input sequence to reflect speaker information in dialogue by applying bert s @xcite and speaker embedding of sa-bert @xcite . then, to better extract the representations of each turn from the encoded input sequence, masked multi-head self-attention @xcite is applied using a surrounding turn mask. next, tucore-gcn constructs a heterogeneous dialogue graph to capture the relational information between arguments in the dialogue. it consists of four types of nodes, namely dialogue node, turn node, subject node, object node, and three different types of edges, i.e., speaker edge, dialogue edge, and argument edge. then, the sequential characteristics of the turn nodes should be considered. to obtain a surrounding turn-aware representation for each node, we apply bidirectional lstm (bilstm) @xcite to the turn nodes and a graph convolutional network @xcite to the heterogeneous dialogue graph. finally, we classify the relations between arguments with the obtained features. the task of emotion recognition in conversations (erc) aims to identify the emotion of utterances in dialogue. erc is a challenging task that has recently gained popularity due to its potential applica- speaker utterance emotion monica he is so cute. so, where did joyful you guys grow up? angela brooklyn heights. neutral bob cleveland. neutral monica how, how did that happen? neutral joey oh my god. scared monica what? neutral joey i suddenly had the feeling that scared i was falling. but i'm not. in summary, our main contributions are as follows: ## experiments in this section, we report our experimental results on two tasks, dialogue-based re and erc. we experiment with two versions of tucore-gcn, tucore-gcn bert and tucore-gcn robert a , respectively based on the uncased base model of bert @xcite and the large model of roberta @xcite . tucore-gcn is trained using adam (kingma and ba, 2015) as an optimizer with weight decay 0.01. we run each experiment five times and report the average score along with the standard deviation (σ) for each metric. the full details of our training settings are provided in the appendix. ## conclusion and future work in this paper, we propose tucore-gcn for dialogue-based re. tucore-gcn is designed according to the way people understand dialogues in practice to better cope with dialogue-based re. in addition, we propose a way to treat the erc task as dialogue-based re and showed its effectiveness through experiments. experimental results on a dialogue-based re dataset and three erc task datasets demonstrate that tucore-gcn model significantly outperforms existing models and yields the new state-of-the-art results on both tasks. since tucore-gcn is modeled for the dialogue text type, we expect it to perform well in dialogue-based natural language understanding tasks. in future work, we are going to explore the effectiveness of it on other dialogue-based tasks.