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A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair

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A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair Rebecca Knowles John E. Ortega Philipp Koehn Department of Computer Science Dept. de Llenguatges Department of Computer Science Johns Hopkins University i Sistemes Informatics` Johns Hopkins University [email protected] Universitat d’Alacant [email protected] [email protected] Abstract the number of changes they need to make in order to complete the translation. A “perfectly” repaired seg- Fuzzy-match repair (FMR), which combines ment would require no changes from the translator. a human-generated translation memory (TM) Ortega et al.(2014) and Ortega et al.(2016) present with the flexibility of machine translation an algorithm for fuzzy-match repair (FMR) using any (MT), is one way of using MT to augment re- source of bilingual information (SBI) as a black-box. sources available to translators. We evaluate Using Apertium (Forcada et al., 2011) as their black- rule-based, phrase-based, and neural MT sys- box machine translation (MT) system, they find that tems as black-box sources of bilingual infor- the best fuzzy-match repaired segments are closer to mation for FMR. We show that FMR success the reference translations than either MT or TM alone. varies based on both the quality of the MT sys- We extend that work by comparing three types of MT tem and the type of MT system being used. systems (rule-based, phrase-based, and neural) as the 1 Introduction source of bilingual information and by examining the way that both MT system quality and type impact per- Translation memories (TM) play a key role in formance. computer-aided translation (CAT) tools: helping trans- We begin with a discussion of related work. In Sec- lators to reuse past work (i.e. when translating highly- tions3 and4, we describe the algorithm used in FMR repetitive texts) by showing them parallel language re- and the MT systems we tested as sources of bilingual sources similar to the text at hand (Bowker, 2002). A information, respectively. Then, in Section5 we show TM consists of pairs of segments in the source and tar- that while phrase-based statistical machine translation get language that were produced by past human trans- (henceforth SMT) and neural MT (henceforth NMT) lation work. In this work, we focus on the fuzzy-match systems both outperform a rule-based (RB) system, repair (FMR)1 task: automatically modifying target- these two types of systems perform in markedly dif- language TM text before providing it to the human ferent ways as black-box input to the FMR system. translator, a task similar to automatic post-editing. Given a new source segment s0 to translate, a CAT 2 Related Work tool can provide the translator with the best fuzzy- Attempting to “repair” and propose translations that match segment s found in the TM and its correspond- are closer to the desired translation is a common ap- ing validated translation segment t. The translator can proach to combining TMs and MT. Simard and Isabelle modify mismatched sub-segments2 of t to produce a (2009); He et al.(2010); Koehn and Senellart(2010) all correct translation of the new segment s0, rather than combine TMs and statistical MT in ways that require translating it from scratch. The goal of FMR is to use either a glass-box or explicitly modified MT. a source of bilingual information (for example, a dic- Our work focuses on ways of applying any MT sys- tionary, MT system, phrase table, etc.) to translate tem to the task of FMR, without requiring knowledge the mismatched sub-segments and correctly combine of the system’s inner workings. We use the approach them with the target segment prior to presenting it to from Ortega et al.(2016) (described in more detail in the translator. Delivering a correctly repaired segment Section3). That particular fuzzy-match repair system should save the human translator time, by decreasing allows the CAT tool to use any source of bilingual in- 1Or fuzzy-match post-editing (Kranias and Samiotou, formation, but in their publications, they focus only on 2004). The use of the term “fuzzy-match” references the Apertium (Forcada et al., 2011) as the source of bilin- fuzzy-match score used to find similar source sentences. gual information. Their work, as well as ours in this 2 Throughout this work, we refer to a complete line of text paper, depends on an oracle evaluation. In order to be as a segment (rather than a sentence, as a number of the lines of text in the data we use do not constitute full grammatical truly useful in a live system, FMR will require some sentences, but may include things like titles). Sequences of form of quality estimation in order to select the best one or more tokens within the segment are sub-segments. repaired segment. Research in that area is ongoing. In the trade-off between adequacy (translations with (Koehn, 2009)) from s and s0 respectively. The (σ; σ0) the same meaning as the source) and fluency (trans- are passed to the black-box MT system for translation, lations that sound fluid or natural), neural machine producing output translations (µ, µ0). To constrain the translation systems, tend towards greater fluency, while set which can be used for repairs, any pair (µ, µ0) for sometimes producing fluent-sounding but semantically which µ is not found in t is discarded. The remain- inappropriate output (Bojar et al., 2016; Koehn and ing (µ, µ0) pairs are then used to “patch” or repair t, Knowles, 2017; Toral and Sanchez-Cartagena´ , 2017). by swapping the µ found in t for the new µ0 in the In the FMR application, the full segment from the hopes of editing t into an accurate translation of s0. translation memory may already provide the (fluent) More than one such patching action can be applied in backbone for the translation, while only containing a the process of forming the final repaired segment, and few subsegment mismatches (such as numbers, names, the system may output multiple unique final repaired noun phrases, and so on). This differs from automatic segments (using different subsets of the set of available post-editing, where there may be structural issues to (µ, µ0) pairs). repair as a result of errors in the machine translation output. All of this naturally raises the question of 4 Data and Machine Translation Systems how rule-based MT (which may provide greater ade- quacy for individual subsegments) will compare to neu- We compare representatives of three MT paradigms: Apertium (rule-based, or RB), Moses (phrase-based ral MT systems (which may provide greater fluency) 4 or phrase-based statistical MT systems (which may fall SMT) and Nematus (NMT with attention). Test data between the two) for the task of FMR. We also address for the FMR experiments is drawn from the 2015 DGT- the question of how NMT systems, which are partic- TM data set which is composed of highly-repetitive ularly sensitive to changes in domain or style (Koehn and formal official legal acts and is lowercased in post- and Knowles, 2017) will perform when used to trans- processing (Steinberger et al., 2012). We choose En- glish to Spanish as the language pair and translation late sub-segments rather than full sentences. 5 Neural MT systems have recently produced state-of- direction. the-art performance across a number of language pairs 4.1 Rule-Based MT (Apertium) (Bojar et al., 2017). While NMT has been applied to other CAT applications, namely interactive translation Apertium (Forcada et al., 2011) is a rule-based (RB) prediction, (Knowles and Koehn, 2016; Wuebker et al., machine translation system, which performs translation 2016) and neural approaches have been used for auto- using a pipeline of components: a morphological ana- matic post-editing (Pal et al., 2016; Junczys-Dowmunt lyzer, a part of speech tagger, a lexical transfer module and Grundkiewicz, 2016; Hokamp, 2017), this is the (which uses a bilingual dictionary to translate lexical first work we are aware of that uses NMT for FMR. forms from source language to target), and a structural transfer module (which performs syntactic operations). 3 Black-Box MT for FMR We use a recent version6 as a baseline. Here we provide an overview of an algorithm for us- 4.2 Neural MT (Nematus) ing black-box MT for FMR. For full details, see Or- We use the attention-based encoder-decoder Nematus tega et al.(2016) (Sections 2 and 3), whose algorithm (Sennrich et al., 2017) and the compatible AmuNMT we follow. Black-box approaches allow one system to decoder7 (Junczys-Dowmunt et al., 2016). be used for many tasks, rather than requiring specially- Initial model training is done using Europarl v7 tailored MT systems for every task. 8 0 (Koehn, 2005) and News Commentary v10 data Given a new source-language sentence s to trans- (WMT13 training data for English–Spanish), with late, the FMR system selects (by fuzzy-match score, 2012 News Test data for validation. Following the do- or FMS) the source-target pair of segments (s; t) from 0 main adaptation method described in Luong and Man- the TM that most closely matches s . The FMS takes ning(2015) and Freitag and Al-Onaizan(2016), we on values between 0% (entire segment requires edits) continue training on DGT-TM 2011–2013, with 3000 to 100% (segments identical). A common definition of FMS3 is given by: 4Due to limited space, we present the best system trained for each MT type. Other systems trained, which included ED(s; s0) FMS(s; s0) = 1 − × 100% (1) ones trained on more directly comparable training data, max(jsj; js0j) showed the same trends.
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