Question: How do they derive a candidate's target phrase set?
Evidence: The past several years have witnessed a significant progress in Neural Machine Translation (NMT). Most NMT methods are based on the encoder-decoder architecture BIBREF0 , BIBREF1 , BIBREF2 and can achieve promising translation performance in a variety of language pairs BIBREF3 , BIBREF4 , BIBREF5 .
However, recent studies BIBREF6 , BIBREF7 show that NMT often generates words that make target sentences fluent, but unfaithful to the source sentences. In contrast, traditional Statistical Machine Translation (SMT) methods tend to rarely make this kind of mistakes. Fig. 1 shows an example that NMT makes mistakes when translating the phrase “jinkou dafu xiahua (the sharp decline in imports)” and the phrase “maoyi shuncha (the trade surplus)”, but SMT can produce correct results when translating these two phrases. BIBREF6 argues that the reason behind this is the use of distributed representations of words in NMT makes systems often generate words that seem natural in the context, but do not reflect the content of the source sentence. Traditional SMT can avoid this problem as it produces the translations based on phrase mappings.
Therefore, it will be beneficial to combine SMT and NMT to alleviate the previously mentioned problem. Actually, researchers have made some effective attempts to achieve this goal. Earlier studies were based on the SMT framework, and have been deeply discussed in BIBREF8 . Later, the researchers transfers to NMT framework. Specifically, coverage mechanism BIBREF9 , BIBREF10 , SMT features BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 and translation lexicons BIBREF6 , BIBREF16 , BIBREF17 have been fully explored. In contrast, phrase translation table, as the core of SMT, has not been fully studied. Recently, BIBREF18 and BIBREF19 explore the possibility of translating phrases in NMT. However, the “phrase” in their approaches are different from that used in phrase-based SMT. In BIBREF18 's models, the phrase pair must be a one-to-one mapping with a source phrase having a unique target phrase (named entity translation pairs). In BIBREF19 's models, the source side of a phrase pair must be a chunk. Therefore, it is still a big challenge to incorporate any phrase pair in the phrase table into NMT system to alleviate the unfaithfulness problem.
In this paper, we propose an effective method to incorporate a phrase table as recommendation memory into the NMT system. To achieve this, we add bonuses to the words in recommendation set to help NMT make better predictions. Generally, our method contains three steps. 1) In order to find out which words are worthy to recommend, we first derive a candidate target phrase set by searching the phrase table according to the input sentence. After that, we construct a recommendation word set at each decoding step by matching between candidate target phrases and previously translated target words by NMT. 2) We then determine the specific bonus value for each recommendable word by using the attention vector produced by NMT and phrase translation probability extracted from phrase table. 3) Finally we integrate the word bonus value into the NMT system to improve the final results.
In this paper, we make the following contributions:
1) We propose a method to incorporate the phrase table as recommendation memory into NMT system. We design a novel approach to find from the phrase table the target words worthy of recommendation, calculate their recommendation scores and use them to promote NMT to make better predictions.
2) Our empirical experiments on Chinese-English translation and English-Japanese translation tasks show the efficacy of our methods. For Chinese-English translation, we can obtain an average improvement of 2.23 BLEU points. For English-Japanese translation, the improvement can reach 1.96 BLEU points. We further find that the phrase table is much more beneficial than bilingual lexicons to NMT.
Generally, our method contains three steps. 1) In order to find out which words are worthy to recommend, we first derive a candidate target phrase set by searching the phrase table according to the input sentence.
Answer: By searching the phrase table according to the input sentence.