All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass

• URL: http://arxiv.org/abs/2205.10744v1
• Date: Sun, 22 May 2022 05:16:03 GMT
• Title: All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
• Authors: Jiaxin Huang, Tianqi Liu, Jialu Liu, Adam D. Lelkes, Cong Yu, Jiawei Han
• Abstract summary: In web content classification, multiple classification tasks are predicted from same input text such as a web article. Existing multitask transformer models need to conduct N forward passes for N tasks with O(N) cost. We propose a scalable method that can achieve stronger performance with close to O(1) computation cost via only one forward pass.
• Score: 34.85886030306857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks. In real industrial applications such as web content classification, multiple classification tasks are predicted from the same input text such as a web article. However, at the serving time, the existing multitask transformer models such as prompt or adaptor based approaches need to conduct N forward passes for N tasks with O(N) computation cost. To tackle this problem, we propose a scalable method that can achieve stronger performance with close to O(1) computation cost via only one forward pass. To illustrate real application usage, we release a multitask dataset on news topic and style classification. Our experiments show that our proposed method outperforms strong baselines on both the GLUE benchmark and our news dataset. Our code and dataset are publicly available at https://bit.ly/mtop-code.

Related papers

• Residual Multi-Task Learner for Applied Ranking [11.774841918446137]
  ResFlow is a lightweight multi-task learning framework that enables efficient cross-task information sharing. It is fully deployed in the pre-rank module of Shopee Search.
  arXiv  Detail & Related papers  (2024-10-30T06:49:45Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification [37.824031151922604]
  We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks. For learning across similar tasks, we employ a multi-task meta-knowledge acquisition (MMA) procedure to train a meta-learner. For learning across distant tasks, we inject the task type descriptions into the prompt, and capture the intra-type and inter-type prompt embeddings.
  arXiv  Detail & Related papers  (2023-08-29T04:16:57Z)
• Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning [43.639430661322585]
  We propose multitask prompt tuning (MPT) MPT learns a single transferable prompt by distilling knowledge from multiple task-specific source prompts. We then learn multiplicative low rank updates to this shared prompt to efficiently adapt it to each downstream target task.
  arXiv  Detail & Related papers  (2023-03-06T03:25:59Z)
• Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations [63.04466647849211]
  Methods typically encode task information with a simple dataset name as a prefix to the encoder. We propose compositional task configurations, a set of prompts prepended to the encoder to improve cross-task generalization. We show this not only allows the model to better learn shared knowledge across different tasks at training, but also allows us to control the model by composing new configurations.
  arXiv  Detail & Related papers  (2022-12-17T02:20:14Z)
• Instance-wise Prompt Tuning for Pretrained Language Models [72.74916121511662]
  Instance-wise Prompt Tuning (IPT) is the first prompt learning paradigm that injects knowledge from the input data instances to the prompts. IPT significantly outperforms task-based prompt learning methods, and achieves comparable performance to conventional finetuning with only 0.5% - 1.5% of tuned parameters.
  arXiv  Detail & Related papers  (2022-06-04T10:08:50Z)
• Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data [82.92758444543689]
  Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge. Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks. Experimental results show that this simple method can achieve significantly better performance on a variety of NLU and NLG tasks.
  arXiv  Detail & Related papers  (2022-03-16T17:37:27Z)
• Active Multi-Task Representation Learning [50.13453053304159]
  We give the first formal study on resource task sampling by leveraging the techniques from active learning. We propose an algorithm that iteratively estimates the relevance of each source task to the target task and samples from each source task based on the estimated relevance.
  arXiv  Detail & Related papers  (2022-02-02T08:23:24Z)
• Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data [5.689320790746046]
  Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer. We propose a novel Transformer architecture consisting of a new conditional attention mechanism and a set of task-conditioned modules.
  arXiv  Detail & Related papers  (2020-09-19T02:04:34Z)
• HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections [96.64246471034195]
  We propose textscHyperGrid, a new approach for highly effective multi-task learning. Our method helps bridge the gap between fine-tuning and multi-task learning approaches.
  arXiv  Detail & Related papers  (2020-07-12T02:49:16Z)

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.