Link prediction for knowledge graphs is the task of predicting missing
relationships between entities. Previous work on link prediction has focused on
shallow, fast models which can scale to large knowledge graphs. However, these
models learn less expressive features than deep, multi-layer models -- which
potentially limits performance. In this work, we introduce ConvE, a multi-layer
convolutional network model for link prediction, and report state-of-the-art
results for several established datasets. We also show that the model is highly
parameter efficient, yielding the same performance as DistMult and R-GCN with
8x and 17x fewer parameters. Analysis of our model suggests that it is
particularly effective at modelling nodes with high indegree -- which are
common in highly-connected, complex knowledge graphs such as Freebase and
YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer
from test set leakage, due to inverse relations from the training set being
present in the test set -- however, the extent of this issue has so far not
been quantified. We find this problem to be severe: a simple rule-based model
can achieve state-of-the-art results on both WN18 and FB15k. To ensure that
models are evaluated on datasets where simply exploiting inverse relations
cannot yield competitive results, we investigate and validate several commonly
used datasets -- deriving robust variants where necessary. We then perform
experiments on these robust datasets for our own and several previously
proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal
Rank across most datasets.

本研究设计了一种多层卷积网络模型 ConvE 用于知识图谱的链接预测，针对多种常见数据集进行了实验，获得了与 DistMult 和 R-GCN 相媲美的性能，极大提高了参数效率，并发现简单基于规则的模型可以获得 WN18 和 FB15k 数据集的最优效果

二维卷积知识图谱嵌入

Convolutional 2D Knowledge Graph Embeddings

This paper presents a novel approach for multi-lingual sentiment
classification in short texts. This is a challenging task as the amount of
training data in languages other than English is very limited. Previously
proposed multi-lingual approaches typically require to establish a
correspondence to English for which powerful classifiers are already available.
In contrast, our method does not require such supervision. We leverage large
amounts of weakly-supervised data in various languages to train a multi-layer
convolutional network and demonstrate the importance of using pre-training of
such networks. We thoroughly evaluate our approach on various multi-lingual
datasets, including the recent SemEval-2016 sentiment prediction benchmark
(Task 4), where we achieved state-of-the-art performance. We also compare the
performance of our model trained individually for each language to a variant
trained for all languages at once. We show that the latter model reaches
slightly worse - but still acceptable - performance when compared to the single
language model, while benefiting from better generalization properties across
languages.

本文提出了一种新的多语言情感分类方法，使用了大量的弱监督数据训练多层卷积网络，通过在多种语言数据集上进行充分的评估，证明该方法的优异性能，达到了同类研究的最高水平。