Neural Natural Language Inference Models Enhanced with External Knowledge (1711.04289v3)

Abstract: Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.

Neural Natural Language Inference Models Enhanced with External Knowledge

In the paper "Neural Natural Language Inference Models Enhanced with External Knowledge," Chen et al. explore an advanced approach to improving neural natural language inference (NLI) models by integrating external knowledge. Recognizing that NLI, which evaluates the inferential relationship between a premise and a hypothesis, poses significant challenges in NLP, the authors investigate whether neural-network-based models can benefit from external knowledge sources, beyond the large quantities of annotated data traditionally used for training.

Core Contributions

The paper's primary contribution lies in the enhancement of existing neural NLI models with external knowledge, demonstrating the method's effectiveness on the SNLI and MultiNLI datasets. Notably, the integration leads to state-of-the-art performance, particularly when training data is limited—a scenario where external knowledge proves most beneficial. The paper uses ESIM (Enhanced Sequential Inference Model) as a baseline, further elevating its performance on benchmark datasets through the proposed methodology.

Methodology

External Knowledge Integration: The paper discusses three main components of neural networks where external knowledge is integrated:

1. Knowledge-Enriched Co-Attention: This involves enhancing word alignment between premises and hypotheses using knowledge about lexical relations such as synonymy, antonymy, hypernymy, and hyponymy derived from WordNet.
2. Local Inference Collection: The model captures word-level inference by incorporating semantic relations, thus refining the identification of entailment, contradiction, or neutrality between premises and hypotheses.
3. Inference Composition: Neural networks utilize knowledge-infused pooling techniques to compose inference information into final decision making.

The paper reveals that external knowledge notably enhances performance in scenarios with restricted training data, while the contribution of knowledge becomes more pronounced during local inference collection even when training data is ample.

Results and Impact

The experimental results affirm that the proposed model, referred to as KIM (Knowledge-based Inference Model), achieves superior accuracy compared to established benchmarks, including a significant improvement in handling lexical inference on a newly introduced test set. The most profound gains are observed in data-scarce environments, elucidating the latent potential of external knowledge in enhancing neural models' comprehension capabilities.

Implications and Future Work

The integration of external knowledge represents a meaningful advancement in natural language understanding, with broader implications for other challenging NLP tasks. The results substantiate the hypothesis that human-accumulated knowledge can empower neural networks to achieve nuanced comprehension, even in the absence of vast annotated datasets. Future research could explore extending this approach to other forms of external knowledge, such as domain-specific corpora, or leveraging advanced graph embedding techniques like TransE more effectively. Moreover, investigating the transferability and scalability of knowledge-enhanced models across diverse NLP tasks promises potential avenues for further exploration.

In summary, the paper marks a significant contribution to neural NLI modeling by illustrating how the integration of external knowledge can surmount the limitations of training data, fostering advancements in the accuracy and reliability of inference-based NLP tasks.