CSAGN : Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling (2021)

Abstract: Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this problem. Our model is based on a conversational structure aware graph network which explicitly encodes the speaker dependent information. We also propose a multi-task learning method to further improve the model. Experimental results on benchmark datasets show that our model with our proposed training objectives significantly outperforms previous baselines.

## introduction recent research has achieved impressive improvements on conversation-based tasks, such as dialogue response generation @xcite @xcite , task-oriented dialogue modeling @xcite and conversational question answering @xcite . however, the frequent occurrences of ellipsis and anaphora in human conversations still create huge challenges for dialogue understanding. to address this, @xcite proposed the conversational semantic role labeling (csrl) task whose goal is to extract predicate-argument structures across the entire conversation. figure 1 illustrates an example, where a csrl parser needs to identify "《泰坦尼克 号》(titanic)" as the arg1 argument of the predicate "看过 (watched)" and the arg0 argument of the predicate "是 (is)". one can see that in the original conversation, "《泰坦尼克号》(titanic)" is omitted in the second turn and referred as "这 (this)" in the last turn. @xcite has demonstrated a: (have ) (watched) (movie) arg0 arg1 arg0 arg1 (titanic) ? b: ( (have not) (watched) , what kind of the movie is it ? a: (this) (is) ) (an american disaster film) the usefulness of csrl on many downstream tasks such as dialogue generation and dialogue rewriting. despite the successes that csrl has achieved, existing csrl model @xcite is a simple extension of bert. specifically, they first encode each utterance into local contextual representations with pre-trained language models, and then utilize a stack of self-attention layers to obtain global contextual representations. we argue that their model may suffer two main problems. first, in the local feature extraction phase, they ignore the fact that jointly considering the predicate and context utterances could help the model better identify some relevant ommited arguments. second, in the global feature extraction phase, some vital conversational structural information, such as the speaker information, is not properly encoded in their model. indeed, speaker-dependent information is necessary for modelling inter-speaker and intra-speaker dependency, both of which could help the model to better handle coreference resolution and zero pronoun resolution. motivated by the above observations, we propose a new csrl model, which consists of three main components. first, we use a pre-trained language model to generate local contextual representations for tokens (sec. 2.1), which is similar to @xcite . then, we propose a new attention strategy to learn predicate-aware contextual representations for tokens (sec. 2.2). finally, we propose a conversational structure aware graph no predicate predicate contained × h 1 h 4 h 2 h 3 gcn conversational structure aware graph network predicate-aware mha input representation predicate-aware utterance rep. csrl dec. u 4 u 4 × n predicate-aware mha ... u 1 u 2 u 3 u 4 u i u i speaker1 speaker2 (a) architecture of csagn 1 0 (b) predicate-aware attention (c) node type and edge type e p concat & norm utt. type decoder p t csrl loss h k p t e t intra-arg. loss h k g k ut loss multi-task learning network (csagn) for learning high-level structural features to represent utterances (sec. 2.3). the resulted utterance representations are incorporated with token representations obtained in the previous two components. with the enhanced token representations, our model predicts the arguments for the given predicate. in addition, we introduce a multi-task learning method with two new objectives. experimental results on benchmark datasets show that our model substantially outperforms existing baselines. our proposed training objectives could also help the model to better learn predicate-aware token representations and structure-aware utterance representations. our code is publicly available at https: //github.com/syxu828/csrl_dataset . ## model the overall architecture of csagn is illustrated in figure 2 which consists of three main components, and we introduce them as follows: ## multi-task learning in this section, we describe how to train the model, based on the representations e, p, g and h. srl objective. formally, given an input conversation x, this objective is to minimize the negative log likelihood of the corresponding correct label sequence y. our model predict the corresponding label y t based on the token representation p t and its corresponding utterance representation h k : intra-argument objective. the arguments in csrl could be categorized into two classes, i.e., the intra-and cross-arguments, where the former are in the same dialogue turns as the predicate while the latter usually occur in the dialogue history. intuitively, the model should be able to identify the intra-arguments without using the dialogue contextual information. motivated by this observation, we introduce a new loss function that only uses e and p to predict the intra-arguments: utterance type objective. we additionally introduce a utterance type objective to learn better utterance representations. specifically, we classify all utterances into three categories, namely predicate-utterance (utterances containing the predicate), argument-utterance (utterances containing arguments) and irrelevant-utterance (utterances without any arguments). we use utterance representations g and h to classify the utterance type: finally, we jointly consider these three types of loss: ## experiments we evaluate our model on three datasets, i.e., duconv @xcite , newsdialog @xcite and personaldialog @xcite . 1 duconv is a multi-turn dialogue dataset that focuses on the domain of movie and star, while news-dialog and personaldialog are open-domain dialogue datasets. 2 we use the same train/dev/test split as @xcite : duconv annotations are splitted into 80%/10%/10% as train/dev/in-domain test set while the newsdialog and personaldialog annotations are treated as the out-domain test set. the hyper-parameters used in our model are listed as follows. the hop size and embedding dimension of csagn is set to 4 and 100, respectively. the α 1 , α 2 , α 3 are set to 1.0, 1.0 and 1.0, respectively. the batch size is set to 128. results and discussion. we used the microaverage f1 scores over the (predicate, argument, label) tuples. following @xcite , we also evaluate f1 scores over intra-and cross-arguments. we compare with two baselines that use different strategies to encode the dialogue history and speaker information. in particular, simplebert @xcite uses the bert as their backbone and simply concatenates the entire dialogue history with the predicate; csrl-bert @xcite also uses the bert but attempts to encode the dialogue structural information by integrating the dialogue turn and speaker embeddings in the input embedding layer. we can see that our model significantly outperforms existing baselines on both the in-domain and out-domain datasets. we can also see that our model benefits from the multi-task training. in particular, when only using the srl objective, the f1 all scores drop by 0.37, 2.16 and 2.28 on three datasets. without using either the intra-argument or utterance-type objective, the performances on all datasets also decrease. moreover, we observe that introducing the intra-argument objective could consistently improve f1 intra while the utterancetype objective is more important to the f1 cross . we think this is because (1) the intra-argument objective denoises the noise from other irrelevant information within the dialogue context; (2) identifying cross-arguments requires a better understanding of the global dialogue structures. let us first look at the impact of different components on our model. from recall that, we model two types of dependencies in our graph neural network, i.e., the speaker and predicate dependency. we investigate the impact of each dependency on our model and results are shown in ## conclusion in this paper, we propose a conversational structure aware graph network for the task of conversational semantic role labeling and a multi-task learning method. experimental results on the benchmark dataset show that our method significantly outperforms previous baselines and achieves the state-of-the-art performance.