Structural Bias for Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2209.00820v1
• Date: Fri, 2 Sep 2022 05:02:18 GMT
• Title: Structural Bias for Aspect Sentiment Triplet Extraction
• Authors: Chen Zhang, Lei Ren, Fang Ma, Jingang Wang, Wei Wu, Dawei Song
• Abstract summary: Structural bias has been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. It is recognized that explicitly incorporating structural bias would have a negative impact on efficiency, whereas pretrained language models (PLMs) can already capture implicit structures. We propose to address the efficiency issues by using an adapter to integrate structural bias in the PLM and using a cheap-to-compute relative position structure.
• Score: 15.273669042985883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly incorporating structural bias would have a negative impact on efficiency, whereas pretrained language models (PLMs) can already capture implicit structures. Thus, a natural question arises: Is structural bias still a necessity in the context of PLMs? To answer the question, we propose to address the efficiency issues by using an adapter to integrate structural bias in the PLM and using a cheap-to-compute relative position structure in place of the syntactic dependency structure. Benchmarking evaluation is conducted on the SemEval datasets. The results show that our proposed structural adapter is beneficial to PLMs and achieves state-of-the-art performance over a range of strong baselines, yet with a light parameter demand and low latency. Meanwhile, we give rise to the concern that the current evaluation default with data of small scale is under-confident. Consequently, we release a large-scale dataset for ASTE. The results on the new dataset hint that the structural adapter is confidently effective and efficient to a large scale. Overall, we draw the conclusion that structural bias shall still be a necessity even with PLMs.

Related papers

• Revisiting Essential and Nonessential Settings of Evidential Deep Learning [70.82728812001807]
  Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation. We propose Re-EDL, a simplified yet more effective variant of EDL.
  arXiv  Detail & Related papers  (2024-10-01T04:27:07Z)
• Learning to Extract Structured Entities Using Language Models [52.281701191329]
  Recent advances in machine learning have significantly impacted the field of information extraction. We reformulate the task to be entity-centric, enabling the use of diverse metrics. We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP metric.
  arXiv  Detail & Related papers  (2024-02-06T22:15:09Z)
• SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization [38.37530720506389]
  We propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures. SFP can significantly outperform both structure-based and non-structure-based OOD generalization SOTAs, with accuracy improvement up to 4.72% and 23.35%, respectively.
  arXiv  Detail & Related papers  (2023-05-19T11:46:36Z)
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data [117.13986738340027]
  We develop an emphIterative Reading-then-Reasoning(IRR) approach for solving question answering tasks based on structured data. Our approach can significantly boost the performance of ChatGPT and achieve comparable performance against the full-data supervised-tuning baselines.
  arXiv  Detail & Related papers  (2023-05-16T17:45:23Z)
• StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure [5.2869308707704255]
  StrAE is a Structured Autoencoder framework that through strict adherence to explicit structure, enables effective learning of multi-level representations. We show that our results are directly attributable to the informativeness of the structure provided as input, and show that this is not the case for existing tree models. We then extend StrAE to allow the model to define its own compositions using a simple localised-merge algorithm.
  arXiv  Detail & Related papers  (2023-05-09T16:20:48Z)
• LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model [96.889634747943]
  Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential. We propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system.
  arXiv  Detail & Related papers  (2023-04-13T04:01:14Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)
• Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning [23.938392334438582]
  We propose a novel Spurious Feature-targeted model Pruning framework, dubbed SFP, to automatically explore invariant substructures. SFP can significantly outperform both structure-based and non-structure OOD generalization SOTAs, with accuracy improvement up to 4.72% and 23.35%, respectively.
  arXiv  Detail & Related papers  (2022-12-19T13:51:06Z)
• ISTR: End-to-End Instance Segmentation with Transformers [147.14073165997846]
  We propose an instance segmentation Transformer, termed ISTR, which is the first end-to-end framework of its kind. ISTR predicts low-dimensional mask embeddings, and matches them with ground truth mask embeddings for the set loss. Benefiting from the proposed end-to-end mechanism, ISTR demonstrates state-of-the-art performance even with approximation-based suboptimal embeddings.
  arXiv  Detail & Related papers  (2021-05-03T06:00:09Z)
• Demystifying Inductive Biases for $\beta$-VAE Based Architectures [19.53632220171481]
  We shed light on the inductive bias responsible for the success of VAE-based architectures. We show that in classical datasets the structure of variance, induced by the generating factors, is conveniently aligned with the latent directions fostered by the VAE objective.
  arXiv  Detail & Related papers  (2021-02-12T23:57:20Z)

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.