Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the "long tail" of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering SQuAD1.1 OTF spelling+lemma (single) EM 62.604 # 186
F1 71.968 # 192
Question Answering SQuAD1.1 OTF dict+spelling (single) EM 64.083 # 183
F1 73.056 # 190
Question Answering SQuAD1.1 OTF spelling (single) EM 62.897 # 185
F1 72.016 # 191
Question Answering SQuAD1.1 dev OTF dict+spelling (single) EM 63.06 # 48

Methods


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