We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Sentiment Analysis CR USE_T+CNN (w2v w.e.) Accuracy 87.45 # 3
Sentiment Analysis MPQA USE_T+DAN (w2v w.e.) Accuracy 88.14 # 3
Sentiment Analysis MR USE_T+CNN Accuracy 81.59 # 6
Conversational Response Selection PolyAI Reddit USE 1-of-100 Accuracy 47.7% # 4
Sentiment Analysis SST-2 Binary classification USE_T+CNN (lrn w.e.) Accuracy 87.21 # 41
Semantic Textual Similarity STS Benchmark USE_T Pearson Correlation 0.782 # 16
Subjectivity Analysis SUBJ USE Accuracy 93.90 # 5
Text Classification TREC-6 USE_T+CNN Error 1.93 # 1

Methods used in the Paper


METHOD TYPE
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