Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2210.06629v2
• Date: Sun, 11 Jun 2023 22:47:33 GMT
• Title: Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis
• Authors: Siddharth Varia, Shuai Wang, Kishaloy Halder, Robert Vacareanu, Miguel Ballesteros, Yassine Benajiba, Neha Anna John, Rishita Anubhai, Smaranda Muresan, Dan Roth
• Abstract summary: 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.
• Score: 72.9124467710526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity. Most computational approaches focus on some of the ABSA sub-tasks such as tuple (aspect term, sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity) extraction using either pipeline or joint modeling approaches. Recently, generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task. In this work, we take a step further and propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios. To this end, we fine-tune a T5 model with instructional prompts in a multi-task learning fashion covering all the sub-tasks, as well as the entire quadruple prediction task. In experiments with multiple benchmark datasets, we show that the proposed multi-task prompting approach brings performance boost (by absolute 8.29 F1) in the few-shot learning setting.

Related papers

• Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval [66.61856014573742]
  Text-based person retrieval (TPR) is a challenging task that involves retrieving a specific individual based on a textual description. Previous methods have attempted to align text and image samples in a modal-shared space. We propose an effective bi-directional one-to-many embedding paradigm that offers a clear optimization direction for each sample.
  arXiv  Detail & Related papers  (2024-06-09T03:06:55Z)
• It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance [3.951769809066429]
  We propose PFInstruct, an extension to an instruction learning paradigm by appending an NLP-related task prefix to the task description. This simple approach leads to improved performance across all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average of +5.43 F1-score.
  arXiv  Detail & Related papers  (2024-05-31T08:57:09Z)
• DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis [84.80347062834517]
  We introduce DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction.
  arXiv  Detail & Related papers  (2022-11-10T17:18:20Z)
• Aspect Sentiment Quad Prediction as Paraphrase Generation [53.33072918744124]
  We introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence. We propose a novel textscParaphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form.
  arXiv  Detail & Related papers  (2021-10-02T12:57:27Z)
• A Unified Generative Framework for Aspect-Based Sentiment Analysis [33.911655982545206]
  Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms. There exist seven subtasks in ABSA. In this paper, we redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexes. We exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to-end framework.
  arXiv  Detail & Related papers  (2021-06-08T12:55:22Z)
• Exploring Relational Context for Multi-Task Dense Prediction [76.86090370115]
  We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. We explore various attention-based contexts, such as global and local, in the multi-task setting. We propose an Adaptive Task-Relational Context module, which samples the pool of all available contexts for each task pair.
  arXiv  Detail & Related papers  (2021-04-28T16:45:56Z)
• A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis [9.587513675287829]
  Aspect based sentiment analysis involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Previous approaches fail to solve all subtasks in a unified end-to-end framework. We construct two machine reading comprehension problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing.
  arXiv  Detail & Related papers  (2021-01-04T07:47:53Z)
• Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
  Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint. We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
  arXiv  Detail & Related papers  (2020-04-28T09:15:50Z)

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.