Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data? If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset. Reasoning and inference are central to both human and artificial intelligence. Natural language inference (NLI) is concerned with determining whether a natural-language hypothesis h can be inferred from a natural-language premise p. Modeling inference in human language is very challenging but is a basic problem towards true natural language understanding -NLI is regarded as a necessary (if not sufficient) condition for true natural language understanding BID20 .The most recent years have seen advances in modeling natural language inference. An important contribution is the creation of much larger annotated datasets such as SNLI BID5 and MultiNLI BID37 . This makes it feasible to train more complex inference models. Neural network models, which often need relatively large amounts of annotated data to estimate their parameters, have shown to achieve the state of the art on SNLI and MultiNLI BID5 BID25 BID27 BID30 BID26 BID8 BID1 . While these neural networks have shown to be very effective in estimating the underlying inference functions by leveraging large training data to achieve the best results, they have focused on end-toend training, where all inference knowledge is assumed to be learnable from the provided training data. In this paper, we relax this assumption, by exploring whether external knowledge can further help the best reported models, for which we propose models to leverage external knowledge in major components of NLI. Consider an example from the SNLI dataset:• p: An African person standing in a wheat field.• h: A person standing in a corn field.If the machine cannot learn useful or plenty information to distinguish the relationship between wheat and corn from the large annotated data, it is difficult for a model to predict that the premise contradicts the hypothesis.In this paper, we propose neural network-based NLI models that can benefit from external knowledge. Although in many tasks learning tabula rasa achieved state-of-the-art performance, we believe complicated NLP problems such as NLI would benefit from leveraging knowledge accumulated by humans, at least in a foreseeable future when machines are unable to learn that with limited data.A typical neural-network-based NLI model consists of roughly four components -encoding the input sentences, performing co-attention across premise and hypothesis, collecting and computing local inference, and performing sentence-level inference judgment by aggregating or composing local information information. In this paper, we propose models that are capable of leveraging external knowledge in co-attention, local inference collection, and inference composition components. We demonstrate that utilizing external knowledge in neural network models outperforms the previously reported best models. The advantage of using external knowledge is more significant when the size of training data is restricted, suggesting that if more knowledge can be obtained, it may yielding more benefit. Specifically, this study shows that external semantic knowledge helps mostly in attaining more accurate local inference information, but also benefits co-attention and aggregation of local inference. Our enriched neural network-based model for natural language inference with external knowledge, namely KIM, achieves a new state-of-the-art accuracy on the SNLI dataset. The model is equipped with external knowledge in the major informal inference components, specifically, in calculating co-attention, collecting local inference, and composing inference. The proposed models of infusing neural networks with external knowledge may also help shed some light on tasks other than NLI, such as question answering and machine translation.
the proposed models with external knowledge further improve the state of the art on the SNLI dataset.
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