Papers with few-shot scenario
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (2021.findings-acl)
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| Challenge: | Existing approaches to event detection ignore the trigger discrepancy and cause errors. |
| Approach: | They propose a unified model which converts a few-shot tagging problem into a single-shot model by using a Gaussian distribution. |
| Outcome: | The proposed model performs better than existing identifythen-classify models on a few-shot tagging problem with a double-part taging scheme. |
Few-Shot Charge Prediction with Discriminative Legal Attributes (C18-1)
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| Challenge: | Existing works on charge prediction perform well on high-frequency charges but are not capable of predicting few-shot charges with limited cases. |
| Approach: | They propose an attribute-attentive charge prediction model to infer attributes and charges simultaneously . they propose to use discriminative attributes as the internal mapping between fact descriptions and charges . |
| Outcome: | The proposed model outperforms baseline models on real-world datasets by more than 50% . the proposed model can predict the attributes and charges simultaneously . |
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (2020.acl-main)
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| Challenge: | Recent context-aware models for slot tagging have achieved state-of-the-art performance . however, the presence of OOV( out-of vocab) words significantly degrades the performance of these models. |
| Approach: | They propose a knowledge-enhanced slot tagging model to integrate contextual representation of input text and large-scale lexical background knowledge. |
| Outcome: | The proposed model achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets. |
Few-Shot Table-to-Text Generation with Prototype Memory (2021.findings-emnlp)
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| Challenge: | Neural table-to-text generation models are data-hungry and require large amounts of training data to learn the mapping between tables and texts. |
| Approach: | They propose a framework for table-to-text generation under the few-shot scenario that uses retrieved prototypes and a prototype selector to bridge the structural gap between tables and texts. |
| Outcome: | The proposed framework significantly improves the model performance on three benchmark datasets with state-of-the-art models. |
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports (2024.lrec-main)
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Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski, Patrick Grau, Subhash Chandra Pujari, Annemarie Friedrich
| Challenge: | Abstract: Natural language processing can help with managing large amounts of unstructured information. |
| Approach: | They propose to annotate a CC-BY-SA-licensed dataset of cyber threat reports . they use named entities, temporal expressions, and cybersecurity-specific concepts . |
| Outcome: | The proposed dataset annotates reports with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. |
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion (2020.emnlp-main)
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| Challenge: | Recent attempts to learn static representations of entities and references ignore their dynamic properties. |
| Approach: | They propose to learn static representations of entities and references ignoring their dynamic properties . a neighbor encoder learns entities' roles while a query-aware aggregator learns references' contributions . |
| Outcome: | The proposed approach achieves state-of-the-art results with different few-shot sizes. |
Domain-specific Attention with Distributional Signatures for Multi-Domain End-to-end Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Existing methods to construct multi-domain task-oriented dialogue systems are difficult to extend to new domains due to high cost of data annotation and scarcity of labeled dialogue data. |
| Approach: | They propose a domain attention module that uses distributional signatures to construct multi-domain dialogue systems with limited data. |
| Outcome: | The proposed method outperforms baseline models on most metrics while keeping smaller model scale. |
KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)
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| Challenge: | Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities. |
| Approach: | They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario. |
ESC: Redesigning WSD with Extractive Sense Comprehension (2021.naacl-main)
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| Challenge: | Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories. |
| Approach: | They propose a transformer-based neural architecture for extractive Sense Comprehension to solve a span extraction problem and a new state of the art English WSD task. |
| Outcome: | The proposed model outdoes all of its competitors while relying on three times fewer annotations. |
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification (2022.coling-1)
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| Challenge: | Existing methods for few-shot text classification suffer from overfitting due to the lack of matching between the few amount of samples and complicated models. |
| Approach: | They propose a method to improve model generalization ability to a new task by leveraging a meta-learner via gradient similarity method. |
| Outcome: | The proposed method improves few-shot text classification performance on several benchmarks. |
Retrieval-Augmented Few-shot Text Classification (2023.findings-emnlp)
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| Challenge: | Existing methods for retrieval-augmented text classification are successful in the few-shot scenario with limited retrieval space. |
| Approach: | They propose to use EM-L and R-L to provide task-specific guidance to retrieval metric . they also propose to incorporate retrieved memory alongside parameters for better generalization . |
| Outcome: | The proposed methods perform better on the few-shot scenario with limited retrieval space. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |
Honey or Poison? Solving the Trigger Curse in Few-shot Event Detection via Causal Intervention (2021.emnlp-main)
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| Challenge: | Recent supervised ED approaches have achieved promising performance but require large number of manually annotated event data. |
| Approach: | They propose to overfit the trigger confounder of the context and the result . they propose to intervene on the context via backdoor adjustment during training . |
| Outcome: | The proposed method significantly improves the FSED on ACE05 and MAVEN datasets. |
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
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| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
| Approach: | They propose a novel multimodal prompt model that captures specific aspect terms in a few-shot scenario. |
| Outcome: | The proposed model outperforms baselines on two MABSA-related tasks on a few-shot dataset. |