Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning

• URL: http://arxiv.org/abs/2306.04925v1
• Date: Thu, 8 Jun 2023 04:04:47 GMT
• Title: Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
• Authors: Jaehyung Kim, Jinwoo Shin, Dongyeop Kang
• Abstract summary: In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation. We propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences.
• Score: 76.43827771613127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of largely human-annotated benchmarks has driven the success of deep neural networks in various NLP tasks. To enhance the effectiveness of existing benchmarks, collecting new additional input-output pairs is often too costly and challenging, particularly considering their marginal impact on improving the current model accuracy. Instead, additional or complementary annotations on the existing input texts in the benchmarks can be preferable as an efficient way to pay the additional human cost. In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation. From 'pair-wise' comparisons with respect to the task, the auxiliary preference learning enables the model to learn an additional informative training signal that cannot be captured with 'instance-wise' task labels. To this end, we propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences. Here, we provide three different ways to collect preference signals in practice: (a) implicitly extracting from annotation records (for free, but often unavailable), (b) collecting explicitly from crowd workers (high paid), or (c) pre-trained large language models such as GPT-3 (low paid). Given existing classification NLP benchmarks, we demonstrate that the proposed auxiliary preference learning via P2C on them is effective in improving text classifiers. Our codes are publicly available.

Related papers

• Co-training for Low Resource Scientific Natural Language Inference [65.37685198688538]
  We propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels. By assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines.
  arXiv  Detail & Related papers  (2024-06-20T18:35:47Z)
• One-Shot Learning as Instruction Data Prospector for Large Language Models [108.81681547472138]
  textscNuggets uses one-shot learning to select high-quality instruction data from extensive datasets. We show that instruction tuning with the top 1% of examples curated by textscNuggets substantially outperforms conventional methods employing the entire dataset.
  arXiv  Detail & Related papers  (2023-12-16T03:33:12Z)
• Task-Specific Embeddings for Ante-Hoc Explainable Text Classification [6.671252951387647]
  We propose an alternative training objective in which we learn task-specific embeddings of text. Our proposed objective learns embeddings such that all texts that share the same target class label should be close together. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy.
  arXiv  Detail & Related papers  (2022-11-30T19:56:25Z)
• Distant finetuning with discourse relations for stance classification [55.131676584455306]
  We propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater.
  arXiv  Detail & Related papers  (2022-04-27T04:24:35Z)
• A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets [23.495743195811375]
  We present the Nearest Neighbors Scores Improvement (NNSI) algorithm for automatic data selection and labeling. The NNSI reduces the need for manual labeling by automatically selecting highly-ambiguous samples and labeling them with high accuracy. We demonstrated the use of NNSI on two large-scale, real-life voice conversation systems.
  arXiv  Detail & Related papers  (2022-02-21T11:36:19Z)
• Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort [9.379650501033465]
  We propose a tool that enables researchers to create large, high-quality, annotated datasets with only a few manual annotations. We combine an active learning (AL) approach with a pre-trained language model to semi-automatically identify annotation categories. Our preliminary results show that employing AL strongly reduces the number of annotations for correct classification of even complex and subtle frames.
  arXiv  Detail & Related papers  (2021-12-15T13:14:58Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification [17.237331828747006]
  This work focuses on few-shot relation classification (FSRC) We propose an adaptive mixture mechanism to add label words to the representation of the class prototype. Experiments have been conducted on FewRel under different few-shot (FS) settings.
  arXiv  Detail & Related papers  (2021-01-10T11:25:42Z)
• Active Learning for Coreference Resolution using Discrete Annotation [76.36423696634584]
  We improve upon pairwise annotation for active learning in coreference resolution. We ask annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour.
  arXiv  Detail & Related papers  (2020-04-28T17:17:11Z)

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.