65 research outputs found
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
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
Knowledge representation learning aims at modeling knowledge graph by
encoding entities and relations into a low dimensional space. Most of the
traditional works for knowledge embedding need negative sampling to minimize a
margin-based ranking loss. However, those works construct negative samples
through a random mode, by which the samples are often too trivial to fit the
model efficiently. In this paper, we propose a novel knowledge representation
learning framework based on Generative Adversarial Networks (GAN). In this
GAN-based framework, we take advantage of a generator to obtain high-quality
negative samples. Meanwhile, the discriminator in GAN learns the embeddings of
the entities and relations in knowledge graph. Thus, we can incorporate the
proposed GAN-based framework into various traditional models to improve the
ability of knowledge representation learning. Experimental results show that
our proposed GAN-based framework outperforms baselines on triplets
classification and link prediction tasks.Comment: Accepted to AAAI 201
Leveraging multilingual descriptions for link prediction: Initial experiments
In most Knowledge Graphs (KGs), textual descriptions ofentities are provided in multiple natural languages. Additional informa-tion that is not explicitly represented in the structured part of the KGmight be available in these textual descriptions. Link prediction modelswhich make use of entity descriptions usually consider only one language.However, descriptions given in multiple languages may provide comple-mentary information which should be taken into consideration for thetasks such as link prediction. In this poster paper, the benefits of mul-tilingual embeddings for incorporating multilingual entity descriptionsinto the task of link prediction in KGs are investigate
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc
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