Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

  title={Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction},
  author={Lu Xu and Lu Xu and Yew Ken Chia and Lidong Bing},
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between… Expand
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
  • Chengxi Li, Feiyu Gao, +8 authors Zhi Yu
  • Computer Science
  • ArXiv
  • 2021
SentiPrompt is proposed to use sentiment knowledge enhanced prompts to tune the language model in the unified framework and inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Expand


A Multi-task Learning Framework for Opinion Triplet Extraction
A novel view of ABSA as an opinion triplet extraction task is presented, and a multi-task learning framework to jointly extract aspect terms and opinion terms is proposed, and simultaneously parses sentiment dependencies between them with a biaffine scorer. Expand
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
An interactive multi-task learning network (IMN) is proposed which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level and introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Expand
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis
A Relation-Aware Collaborative Learning (RACL) framework is proposed which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Expand
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A new framework for tackling ATE can exploit two useful clues, namely opinion summary and aspect detection history, and can outperform all state-of-the-art methods. Expand
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms
A novel deep learning model, named coupled multi-layer attentions, where each layer consists of a couple of attentions with tensor operators that are learned interactively to dually propagate information between aspect terms and opinion terms. Expand
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling
This paper proposes a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target through a target-fused sequence labeling neural network model. Expand
Effective Attention Modeling for Aspect-Level Sentiment Classification
This work proposes a method for target representation that better captures the semantic meaning of the opinion target and introduces an attention model that incorporates syntactic information into the attention mechanism. Expand
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Expand
Learning Latent Opinions for Aspect-level Sentiment Classification
A segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer is proposed. Expand
Learning multi-grained aspect target sequence for Chinese sentiment analysis
This paper formalizes the problem of aspect-level sentiment analysis from a different perspective, i.e., that sentiment at aspect target level should be the main focus and proposes to explicitly model the aspect target and conduct sentiment classification directly at the aspect targets level via three granularities. Expand