Question: What is used to provide a basis for filtering with constraints?
Evidence: We automatically create a test set from the OpenSubtitles corpus BIBREF22 . The goal is to provide a large number of difficult test cases where an English pronoun has to be translated to a German pronoun.
The most challenging cases are translating it to either er, sie or es, depending on the grammatical gender of the antecedent. Not only is the translation of it ambiguous, there is also class imbalance in the training data (see Table TABREF18 ). There is roughly a 30% probability that it is aligned to es, which makes it difficult to learn to translate er and sie. We use parsing and automatic co-reference resolution to find translation pairs that satisfy our constraints.
To provide a basis for filtering with constraints, we tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align BIBREF23 , parse the English text with CoreNLP BIBREF24 , parse the German text with ParZu BIBREF25 and perform coreference resolution on both sides. The coreference chains are obtained with the neural model of CoreNLP for English, and with CorZu for German BIBREF26 , respectively.
Then we opt for high-precision, aggressive filtering, according to the following protocol: for each pair of sentences INLINEFORM0 in English and German, extract iff
 INLINEFORM0 contains the English pronoun it, and INLINEFORM1 contains a German pronoun that is third person singular (er, sie or es), as indicated by their part-of-speech tags;
those pronouns are aligned to each other;
both pronouns are in a coreference chain;
their nominal antecedents in the coreference chain are aligned on word level.
This removes most candidate pairs, but is necessary to overcome the noise introduced by our preprocessing pipeline, most notably coreference resolution. From the filtered set, we create a balanced test set by randomly sampling 4000 instances of each of the three translations of it under consideration (er, sie, es). We do not balance antecedent distance. See Table TABREF25 for the distribution of pronoun pairs and antecedent distance in the test set.
For each sentence pair in the resulting test set, we introduce contrastive translations. A contrastive translation is a translation variant where the correct pronoun is swapped with an incorrect one. For an example, see Table TABREF19 , where the pronoun it in the original translation corresponds to sie because the antecedent bat is a feminine noun in German (Fledermaus). We produce wrong translations by replacing sie with one of the other pronouns (er, es).
Note that, by themselves, these contrastive translations are grammatically correct if the antecedent is outside the current sentence. The test set also contains pronouns with an antecedent in the same sentence (antecedent distance 0). Those examples do not require any additional context for disambiguation and we therefore expect the sentence-level baseline to perform well on them.
We take extra care to ensure that the resulting contrastive translations are grammatically correct, because ungrammatical sentences are easily dismissed by an NMT system. For instance, if there are any possessive pronouns (such as seine) in the sentence, we also change their gender to match the personal pronoun replacement.
The German coreference resolution system does not resolve es because most instances of es in German are either non-referential forms, or they refer to a clause instead of a nominal antecedent. We limit the test set to nominal antecedents, as these are the only ambiguous cases with respect to translation. For this reason, we have to rely entirely on the English coreference links for the extraction of sentence pairs with it INLINEFORM0 es, as opposed to pairs with it INLINEFORM1 er and it INLINEFORM2 sie where we have coreference chains in both languages.
Our extraction process respects document boundaries, to ensure we always search for the right context. We extract additional information from the annotated documents, such as the distance (in sentences) between pronouns and their antecedents, the document of origin, lemma, morphology and dependency information if available.
To provide a basis for filtering with constraints, we tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align (Dyer et al., 2013), parse the English text with CoreNLP (Manning et al., 2014), parse the German text with ParZu (Sennrich et al., 2013) and perform coreference resolution on both sides.
Answer: To provide a basis for filtering with constraints, the authors tokenize the whole data set with the Moses tokenizer, generate symmetric word alignments with fast_align, parse the English text with CoreNLP , parse the German text with ParZu and perform coreference resolution on both sides.