Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2412.00208v2
• Date: Fri, 07 Feb 2025 12:12:16 GMT
• Title: Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
• Authors: Xinmeng Hou, Lingyue Fu, Chenhao Meng, Kounianhua Du, Wuqi Wang, Hai Hu,
• Abstract summary: We propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions. Our model achieves the best performance on both ASTE and AOPE if trained on combined datasets.
• Score: 4.372906783600122
• License:
• Abstract: Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these challenges and being inspired by transition-based dependency parsing, we propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly, which also better captures position-aware aspect-opinion relations and mitigates entity-level bias. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions and jointly optimizes separate subtasks in linear time. Extensive experiments on 4 commonly used ASTE/AOPE datasets show that, while performing worse when trained on a single dataset than some previous models, our model achieves the best performance on both ASTE and AOPE if trained on combined datasets, outperforming the strongest previous models in F1-measures (often by a large margin). We hypothesize that this is due to our model's ability to learn transition actions from multiple datasets and domains. Our code is available at https://anonymous.4open.science/r/trans_aste-8FCF.

Related papers

• Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging [33.23758947497205]
  Advanced embedding models are typically developed using large-scale multi-task data and joint training across multiple tasks. To overcome these challenges, we explore model merging-a technique that combines independently trained models to mitigate gradient conflicts and balance data distribution. We introduce a novel method, Self Positioning, which efficiently searches for optimal model combinations within the space of task vectors using gradient descent.
  arXiv  Detail & Related papers  (2024-10-19T08:39:21Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation [15.718287580146272]
  We propose a novel model-agnostic and highly generic framework for sequential recommendation called sample enrichment via temporary operations on subsequences (SETO) We highlight our SETO's effectiveness and versatility over multiple representative and state-of-the-art sequential recommendation models across multiple real-world datasets.
  arXiv  Detail & Related papers  (2024-07-25T06:22:08Z)
• Weak Reward Model Transforms Generative Models into Robust Causal Event Extraction Systems [17.10762463903638]
  We train evaluation models to approximate human evaluation, achieving high agreement. We propose a weak-to-strong supervision method that uses a fraction of the annotated data to train an evaluation model.
  arXiv  Detail & Related papers  (2024-06-26T10:48:14Z)
• Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs. Specifically, we introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer encoder for spatial feature extraction. We benchmark three practical sports game datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model [74.62272538148245]
  We show that for arbitrary pairings of pretrained models, one model extracts significant data context unavailable in the other. We investigate if it is possible to transfer such "complementary" knowledge from one model to another without performance degradation.
  arXiv  Detail & Related papers  (2023-10-26T17:59:46Z)
• Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training [44.790636524264]
  Point Prompt Training is a novel framework for multi-dataset synergistic learning in the context of 3D representation learning. It can overcome the negative transfer associated with synergistic learning and produce generalizable representations. It achieves state-of-the-art performance on each dataset using a single weight-shared model with supervised multi-dataset training.
  arXiv  Detail & Related papers  (2023-08-18T17:59:57Z)
• Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [76.35271072704384]
  Deep learning models perform poorly when applied to videos with rare scenes or objects. We tackle this problem from two different angles: algorithm and dataset. We show that the debiased representation can generalize better when transferred to other datasets and tasks.
  arXiv  Detail & Related papers  (2022-09-20T00:30:35Z)
• Beyond Transfer Learning: Co-finetuning for Action Localisation [64.07196901012153]
  We propose co-finetuning -- simultaneously training a single model on multiple upstream'' and downstream'' tasks. We demonstrate that co-finetuning outperforms traditional transfer learning when using the same total amount of data. We also show how we can easily extend our approach to multiple upstream'' datasets to further improve performance.
  arXiv  Detail & Related papers  (2022-07-08T10:25:47Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)

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.