One Model to Learn Both: Zero Pronoun Prediction and Translation
Table 4: Translation results when no ZP-annotated input is used in decoding by removing the reconstructor component. “\bigtriangledown” denotes the performance gap between whether using the annotated input (“✓”) or not (“×”).
['[BOLD] Model', '[BOLD] ZP-Annotated Input ✓', '[BOLD] ZP-Annotated Input ×', '[BOLD] ZP-Annotated Input \\bigtriangledown']
Some researchers may argue that previous approaches Wang:2018:AAAI are also able to release the reliance of externally annotated input by removing the reconstructor component. Without ZP-annotated input in decoding, all approaches can still outperform the baseline model, by benefiting better intermediate representations that contain necessary ZP information. Compared with reconstruction-based models, however, removing the reconstruction components leads to decrease on translation quality. As seen, the BLEU score of best “External ZP prediction” model dramatically drops by -1.06 points, showing that this approach is heavily dependent on the results of external ZP annotations. The performances of proposed models only decrease by -0.1\sim-0.6 BLEU point. It indicates that our models are compatible with the standard encoder-decoder-reconstructor framework, thus enjoy an additional benefit of re-scoring translation hypotheses in testing with reconstruction scores. All the results together prove the superiority of the proposed unified framework for ZP translation.