14,691 research outputs found
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
Existing attribute-value extraction (AVE) models require large quantities of
labeled data for training. However, new products with new attribute-value pairs
enter the market every day in real-world e-Commerce. Thus, we formulate AVE in
multi-label few-shot learning (FSL), aiming to extract unseen attribute value
pairs based on a small number of training examples. We propose a
Knowledge-Enhanced Attentive Framework (KEAF) based on prototypical networks,
leveraging the generated label description and category information to learn
more discriminative prototypes. Besides, KEAF integrates with hybrid attention
to reduce noise and capture more informative semantics for each class by
calculating the label-relevant and query-related weights. To achieve
multi-label inference, KEAF further learns a dynamic threshold by integrating
the semantic information from both the support set and the query set. Extensive
experiments with ablation studies conducted on two datasets demonstrate that
KEAF outperforms other SOTA models for information extraction in FSL. The code
can be found at: https://github.com/gjiaying/KEAFComment: 6 pages, 2 figures, published in CIKM 202
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is
determined by dice rolling. Visit our website http://zhuhao.me/fewre
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