Conversational Semantic Role Labeling

CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling

“…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 Xu et al (2021): 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.…”

“…However, the frequent occurrences of ellipsis and anaphora in human conversations still create huge challenges for dialogue understanding. To address this, Xu et al (2021) 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. Xu et al (2021) 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 (Xu et al, 2021) 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.…”

“…First, we use a pre-trained language model to generate local contextual representations for tokens (Sec. 2.1), which is similar to Xu et al (2021). Then, we propose a new attention strategy to learn predicate-aware contextual representations for tokens (Sec.…”