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Empirical Evaluation of Active Learning Techniques for Neural MT

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Figure

Figure 1 and Table 1 compares the results ofmodel-driven approaches and random samplingbaseline
Figure 3data-driven approaches (SS) corresponding to dif-ferent sources of embeddings.that using bag-of-words (SS-BoW) and contextualembeddings (SS-CE) results in roughly the sameperformance, barely beating the random baseline.However, fine-tuning the contextual embeddingson the paraphrase task (SS-PE), brings about alarge performance gain, emphasizing the effective-ness of fine-tuning on semantic similarity tasks for shows the results of embedding basedIt is noteworthyAL.
Figure 5: Results of best performing approaches withBEER (B-X) and TER (T-X) evaluation metrics.

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