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Found 8 papers, 5 papers with code
RST Discourse Parsing with Second-Stage EDU-Level Pre-training
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.
Speaker-Aware Discourse Parsing on Multi-Party Dialogues
It is believed that speaker interactions are helpful for this task.
Ranked #2 on
Discourse Parsing
on STAC
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation
In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC.
Ranked #20 on
Emotion Recognition in Conversation
on EmoryNLP
Discourse-Aware Emotion Cause Extraction in Conversations
In this paper, we investigate the importance of discourse structures in handling utterance interactions and conversationspecific features for ECEC.
Ranked #5 on
Causal Emotion Entailment
on RECCON
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous.
Robust numerical computation of the 3D scalar potential field of the cubic Galileon gravity model at solar system scales
Here we present a numerical method based on finite differences for solution of the static CGG scalar field for a 2D axisymmetric Sun-Earth system and a 3D Cartesian Sun-Earth-Moon system.
Computational Physics
Transition-based Neural RST Parsing with Implicit Syntax Features
Syntax has been a useful source of information for statistical RST discourse parsing.
Ranked #7 on
Discourse Parsing
on RST-DT
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems.
