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Qi Ju

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2 papers

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AAAI Conference 2020 Conference Paper

K-BERT: Enabling Language Representation with Knowledge Graph

Weijie Liu
Peng Zhou
Zhe Zhao
Zhiruo Wang
Qi Ju
Haotang Deng
Ping Wang

Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-speciﬁc knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces softposition and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by being equipped with a KG without pre-training by itself because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-speciﬁc tasks (including ﬁnance, law, and medicine), K-BERT signiﬁcantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.

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AAAI Conference 2019 Conference Paper

Improving Image Captioning with Conditional Generative Adversarial Nets

Chen Chen
Shuai Mu
Wanpeng Xiao
Zexiong Ye
Liesi Wu
Qi Ju

In this paper, we propose a novel conditional-generativeadversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture. To deal with the inconsistent evaluation problem among different objective language metrics, we are motivated to design some “discriminator” networks to automatically and progressively determine whether generated caption is human described or machine generated. Two kinds of discriminator architectures (CNN and RNNbased structures) are introduced since each has its own advantages. The proposed algorithm is generic so that it can enhance any existing RL-based image captioning framework and we show that the conventional RL training method is just a special case of our approach. Empirically, we show consistent improvements over all language evaluation metrics for different state-of-the-art image captioning models. In addition, the well-trained discriminators can also be viewed as objective image captioning evaluators.

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