Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Table 4: Effect of the beam size (word beam size Nw + phrase beam size Np) for the hybrid approach on the e-commerce English→Russian task.
['Beam size  [ITALIC] Np', 'Beam size  [ITALIC] Nw', 'Item descr. BLEU', 'Item descr. TER', 'Product descr. BLEU', 'Product descr. TER']
-S4SS1SSSx3 Tuning the beam size Next, we study the effect of different beam sizes on translation quality. Note that we retune the system for each choice. With a total beam size of 128, we get the best results by using a phrase beam size of 96 and a word beam size of 32. When we use a phrase beam size of 116 or 64 instead, the translation quality worsens. In another experiment, we decrease the total beam size to 64. The translation quality degrades only slightly, which means that we can still expect MT quality improvements with hybrid search even if we optimize the system for speed. To further test this, we reduce the beam sizes to Nw=12 and Np=4 after tuning with Nw=32 and Np=96. We get BLEU scores of 27.1% on item descriptions and 30.1% on product descriptions, losing 0.3% and 0.7% BLEU respectively compared to the full beam size.