A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference

By Biswajit Paria, K. Annervaz, Ambedkar Dukkipati, Ankush Chatterjee, Sanjay Podder. 2016

13 citations recorded by the source corpus.

In this work we use the recent advances in representation learning to propose a neural architecture for the problem of natural language inference. Our approach is aligned to mimic how a human does the natural language inference process given two statements. The model uses variants of Long Short Term Memory (LSTM), attention mechanism and composable neural networks, to carry out the task. Each part of our model can be mapped to a clear functionality humans do for carrying out the overall task of natural language inference. The model is end-to-end differentiable enabling training by stochastic gradient descent. On Stanford Natural Language Inference(SNLI) dataset, the proposed model achieves better accuracy numbers than all published models in literature.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhenhua Ling
2. Xiao-Dan Zhu
3. Qian Chen
4. Si Wei
5. Diana Inkpen
6. Hui Jiang
7. Mohit Bansal
8. Ashish Sabharwal

In Computer Vision and Pattern Recognition

1. Zhenhua Ling
2. Qian Chen
3. Xiao-Dan Zhu
4. Diana Inkpen
5. Si Wei
6. Mohit Bansal
7. Hui Jiang
8. Ashish Sabharwal

Citations in the corpus

• Learned in Translation: Contextualized Word Vectors (2017, neutral): “…[Munkhdalai and Yu, 2016b] 87.3 bmLSTM [Radford et al., 2017] 92.9 re-read LSTM [Sha et al., 2016] 87.5 TRNN [Dieng et al., 2016] 93.8 btree-LSTM [Paria et al., 2016] 87.6 oh-LSTM [Johnson and Zhang, 2016] 94.1 600D ESIM [Chen et al., 2016] 88.0 Virtual [Miyato et al., 2017] 94.1 BCN+Char+CoVe…”
• Enhanced LSTM for Natural Language Inference (2016, background): “Sha et al. (2016) proposes a special LSTM variant which considers the attention vector of another sentence as an inner state of LSTM. Paria et al. (2016) use a neural architecture with a complete binary tree-LSTM encoders without syntactic information.”
• Natural Language Inference with External Knowledge (2017, unknown): “04 28 2016; Munkhdalai & Yu, 2016b; Parikh et al., 2016; Sha et al., 2016; Paria et al., 2016; Chen et al., 2017a;b).”
• Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (2016, background): “The original SNLI corpus contains also “ the other ” category, which includes the sentence pairs lacking consensus among multiple human annotators.”