Papers with sentence classification tasks
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data. |
| Approach: | They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs. |
| Outcome: | The proposed task improves on two cross-lingual MRC datasets. |
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples (2020.acl-main)
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| Challenge: | Recent Transformer-based architectures provide impressive results in many NLP tasks, but obtaining high-quality annotated data is expensive and time consuming. |
| Approach: | They propose a semisupervised learning method that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. |
| Outcome: | The proposed method reduces the requirement for annotated examples while achieving good performance in sentence classification tasks. |
How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning (2024.emnlp-main)
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| Challenge: | In-context learning (ICL) is an emergent ability of large language models. |
| Approach: | They propose to use in-context learning to predict sentences with semantically-unrelated labels on 1% heads to investigate the mechanism. |
| Outcome: | The proposed methods reduce the majority label bias and recency bias by 22% and 17%, respectively. |
TextMixer: Mixing Multiple Inputs for Privacy-Preserving Inference (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (PLMs) are often deployed as cloud services, enabling users to upload textual data and perform inference remotely. |
| Approach: | They propose a privacy-preserving inference framework called MixPi which aims to obfuscate a user's private input by mixing it with multiple other inputs. |
| Outcome: | The proposed framework surpasses existing privacy-preserving methods on token and sentence classification tasks. |
TextObfuscator: Making Pre-trained Language Model a Privacy Protector via Obfuscating Word Representations (2023.findings-acl)
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| Challenge: | Existing inference services are plagued by privacy concerns, such as sharing sensitive data with service providers. |
| Approach: | They propose a framework for protecting inference privacy by applying random perturbations to clustered representations. |
| Outcome: | The proposed framework protects inference privacy by applying random perturbations to clustered representations. |
Dynamic Augmentation Data Selection for Few-shot Text Classification (2022.findings-emnlp)
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| Challenge: | Data augmentation is a popular method for fine-tuning pre-trained language models to increase model robustness and performance. |
| Approach: | They propose a dynamic data selection method to select effective augmentation data from different augmentation sources according to the model’s learning stage by identifying a set of augmentation samples that optimally facilitates the learning process of the most current model. |
| Outcome: | The proposed method outperforms strong baselines on a variety of sentence classification tasks. |
Coordination Generation via Synchronized Text-Infilling (2022.coling-1)
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| Challenge: | Generating synthetic data from pre-trained language models has enhanced performance across several NLP tasks. |
| Approach: | They propose a method for generating sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified. |
| Outcome: | The proposed method produces promising coordination instances that provide gains for the task in low-resource settings. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
On the Dimensionality of Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing work focuses on improving the quality of sentence embeddings, but the exploration of sentence dimension is limited. |
| Approach: | They propose a two-step training method where the encoder and pooler are optimized separately to mitigate the overall performance loss in low-dimension scenarios. |
| Outcome: | The proposed method significantly improves the performance of low-dimensional sentence embeddings on seven STS tasks and seven sentence classification tasks. |