Question: What is motivated by the hope for a better generalization over inflectional variants of the same word form？
Evidence: Neural machine translation has recently achieved impressive results BIBREF0 , BIBREF1 , while learning from raw, sentence-aligned parallel text and using little in the way of external linguistic information. However, we hypothesize that various levels of linguistic annotation can be valuable for neural machine translation. Lemmatisation can reduce data sparseness, and allow inflectional variants of the same word to explicitly share a representation in the model. Other types of annotation, such as parts-of-speech (POS) or syntactic dependency labels, can help in disambiguation. In this paper we investigate whether linguistic information is beneficial to neural translation models, or whether their strong learning capability makes explicit linguistic features redundant.
Let us motivate the use of linguistic features using examples of actual translation errors by neural MT systems. In translation out of English, one problem is that the same surface word form may be shared between several word types, due to homonymy or word formation processes such as conversion. For instance, close can be a verb, adjective, or noun, and these different meanings often have distinct translations into other languages. Consider the following English INLINEFORM0 German example:
For the English source sentence in Example SECREF4 (our translation in Example SECREF5 ), a neural MT system (our baseline system from Section SECREF4 ) mistranslates close as a verb, and produces the German verb schließen (Example SECREF6 ), even though close is an adjective in this sentence, which has the German translation nah. Intuitively, part-of-speech annotation of the English input could disambiguate between verb, noun, and adjective meanings of close.
As a second example, consider the following German INLINEFORM0 English example:
German main clauses have a verb-second (V2) word order, whereas English word order is generally SVO. The German sentence (Example UID7 ; English reference in Example UID8 ) topicalizes the predicate gefährlich 'dangerous', putting the subject die Route 'the route' after the verb. Our baseline system (Example UID9 ) retains the original word order, which is highly unusual in English, especially for prose in the news domain. A syntactic annotation of the source sentence could support the attentional encoder-decoder in learning which words in the German source to attend (and translate) first.
We will investigate the usefulness of linguistic features for the language pair German INLINEFORM0 English, considering the following linguistic features:
The inclusion of lemmas is motivated by the hope for a better generalization over inflectional variants of the same word form. The other linguistic features are motivated by disambiguation, as discussed in our introductory examples.
We will investigate the usefulness of linguistic features for the language pair German↔English, considering the following linguistic features: The inclusion of lemmas is motivated by the hope for a better generalization over inflectional variants of the same word form.
Answer: The inclusion of lemmas is motivated by the hope for a better generalization over inflectional variants of the same word form.