Aspect Sentiment Triplet Extraction Using Reinforcement Learning
- URL: http://arxiv.org/abs/2108.06107v1
- Date: Fri, 13 Aug 2021 07:38:48 GMT
- Title: Aspect Sentiment Triplet Extraction Using Reinforcement Learning
- Authors: Samson Yu Bai Jian, Tapas Nayak, Navonil Majumder, and Soujanya Poria
- Abstract summary: We present a novel paradigm, ASTE-RL, by regarding the aspect and opinion terms as arguments of the expressed sentiment.
We first focus on sentiments expressed in a sentence, then identify the target aspect and opinion terms for that sentiment.
This takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency.
- Score: 14.21689018940387
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting triplets
of aspect terms, their associated sentiments, and the opinion terms that
provide evidence for the expressed sentiments. Previous approaches to ASTE
usually simultaneously extract all three components or first identify the
aspect and opinion terms, then pair them up to predict their sentiment
polarities. In this work, we present a novel paradigm, ASTE-RL, by regarding
the aspect and opinion terms as arguments of the expressed sentiment in a
hierarchical reinforcement learning (RL) framework. We first focus on
sentiments expressed in a sentence, then identify the target aspect and opinion
terms for that sentiment. This takes into account the mutual interactions among
the triplet's components while improving exploration and sample efficiency.
Furthermore, this hierarchical RLsetup enables us to deal with multiple and
overlapping triplets. In our experiments, we evaluate our model on existing
datasets from laptop and restaurant domains and show that it achieves
state-of-the-art performance. The implementation of this work is publicly
available at https://github.com/declare-lab/ASTE-RL.
Related papers
- ExpLLM: Towards Chain of Thought for Facial Expression Recognition [61.49849866937758]
We propose a novel method called ExpLLM to generate an accurate chain of thought (CoT) for facial expression recognition.
Specifically, we have designed the CoT mechanism from three key perspectives: key observations, overall emotional interpretation, and conclusion.
In experiments on the RAF-DB and AffectNet datasets, ExpLLM outperforms current state-of-the-art FER methods.
arXiv Detail & Related papers (2024-09-04T15:50:16Z) - PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA)
To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements.
To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
arXiv Detail & Related papers (2024-08-18T13:51:01Z) - Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect
Sentiment Triplet Extraction [63.0205418944714]
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
Recent studies tend to address this task with a table-filling paradigm, wherein word relations are encoded in a two-dimensional table.
We propose a novel model for the ASTE task, called Prompt-based Tri-Channel Graph Convolution Neural Network (PT-GCN), which converts the relation table into a graph to explore more comprehensive relational information.
arXiv Detail & Related papers (2023-12-18T12:46:09Z) - A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction [3.5838781091072143]
Aspect Sentiment Triplet Extraction aims to extract the triplet of an aspect term, an opinion term, and their corresponding sentiment polarity from the review texts.
Due to the complexity of language and the existence of multiple aspect terms and opinion terms in a single sentence, current models often confuse the connections between an aspect term and the opinion term describing it.
We propose a pairing enhancement approach for ASTE, which incorporates contrastive learning during the training stage to inject aspect-opinion pairing knowledge into the triplet extraction model.
arXiv Detail & Related papers (2023-06-11T07:32:10Z) - Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis [72.9124467710526]
generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task.
We propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios.
arXiv Detail & Related papers (2022-10-12T23:38:57Z) - Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction [25.984894351763945]
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA.
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.
Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation.
arXiv Detail & Related papers (2021-07-26T13:47:31Z) - A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task [19.101354902943154]
We introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction Task.
The sentiment in a triplet extracted by ASOTE is the sentiment of the aspect term and opinion term pair.
We build four datasets for ASOTE based on several popular ABSA benchmarks.
arXiv Detail & Related papers (2021-03-29T00:42:51Z) - Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet
Extraction [8.208671244754317]
Aspect sentiment triplet extraction (ASTE) is an emerging task in fine-grained opinion mining.
We transform ASTE task into a multi-turn machine reading comprehension (MTMRC) task.
We propose a bidirectional MRC (BMRC) framework to address this challenge.
arXiv Detail & Related papers (2021-03-13T09:30:47Z) - Opinion Transmission Network for Jointly Improving Aspect-oriented
Opinion Words Extraction and Sentiment Classification [56.893393134328996]
Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis subtasks.
We propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE.
arXiv Detail & Related papers (2020-11-01T11:00:19Z) - Position-Aware Tagging for Aspect Sentiment Triplet Extraction [37.76744150888183]
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets.
We propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets.
arXiv Detail & Related papers (2020-10-06T10:40:34Z) - A Dependency Syntactic Knowledge Augmented Interactive Architecture for
End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA.
This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn)
Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-04T14:59:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.