Search Results for author:
Found 12 papers, 3 papers with code
Exploiting Contextual Target Attributes for Target Sentiment Classification
The attributes contain the background and property information of the target, which can help to enrich the semantics of the review context and the target.
Co-guiding for Multi-intent Spoken Language Understanding
For the first stage, we propose single-task supervised contrastive learning, and for the second stage, we propose co-guiding supervised contrastive learning, which considers the two tasks' mutual guidances in the contrastive learning procedure.
Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding
In this paper, we put forward a new framework, whose core is relational temporal graph reasoning. We propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to facilitate relational temporal modeling in dialog understanding and dual-task reasoning.
Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction
Finally, we propose a Co-evolving Graph Reasoning Network (CGR-Net) that implements our MTL framework and conducts Co-evolving Reasoning on MRG.
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the \textit{mutual guidances} between the two tasks.
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling
Therefore, in this paper, we first construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies: (1) statistical dependencies based on labels' co-occurrence patterns and hierarchies in slot labels; (2) rich relations among the label nodes.
Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning
In this paper, we propose a novel model termed Neural Subgraph Explorer, which (1) reduces the noisy information via pruning target-irrelevant nodes on the syntax graph; (2) introduces beneficial first-order connections between the target and its related words into the obtained graph.
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition
To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions.
DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification
In aspect-level sentiment classification (ASC), state-of-the-art models encode either syntax graph or relation graph to capture the local syntactic information or global relational information.
Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network
Aspect-level sentiment classification (ASC) aims to predict the fine-grained sentiment polarity towards a given aspect mentioned in a review.
Aspect-Based Sentiment Analysis (ABSA)
Sentiment Classification
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis
In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
