Neural Semantic Encoders

EACL 2017  ·  Tsendsuren Munkhdalai, Hong Yu ·

We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read}, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Natural Language Inference SNLI 300D MMA-NSE encoders with attention % Test Accuracy 85.4 # 69
% Train Accuracy 86.9 # 59
Parameters 3.2m # 3
Natural Language Inference SNLI 300D NSE encoders % Test Accuracy 84.6 # 74
% Train Accuracy 86.2 # 62
Parameters 3.0m # 3
Sentiment Analysis SST-2 Binary classification Neural Semantic Encoder Accuracy 89.7 # 53
Question Answering WikiQA MMA-NSE attention MAP 0.6811 # 12
MRR 0.6993 # 10
Machine Translation WMT2014 English-German NSE-NSE BLEU score 17.9 # 72
Hardware Burden None # 1
Operations per network pass None # 1


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