Example-based machine translation using the marker hypothesis

The development of large-scale rules and grammars for a Rule-Based Machine Translation (RBMT) system is labour-intensive, error-prone and expensive. Current research in Machine Translation (MT) tends to focus on the development of corpus-based systems which can overcome the problem of knowledge acquisition. Corpus-Based Machine Translation (CBMT) can take the form of Statistical Machine Translation (SMT) or Example-Based Machine Translation (EBMT). Despite the benefits of EBMT, SMT is currently the dominant paradigm and many systems classified as example-based integrate additional rule-based and statistical techniques. The benefits of an EBMT system which does not require extensive linguistic resources and can produce reasonably intelligible and accurate translations cannot be overlooked. We show that our linguistics-lite EBMT system can outperform an SMT system trained on the same data. The work reported in this thesis describes the development of a linguistics-lite EBMT system which does not have recourse to extensive linguistic resources. We apply the Marker Hypothesis (Green, 1979) — a psycholinguistic theory which states that all natural languages are ‘marked’ for complex syntactic structure at surface form by a closed set of specific lexemes and morphemes. We use this technique in different environments to segment aligned (English, French) phrases and sentences. We then apply an alignment algorithm which can deduce smaller aligned chunks and words. Following a process similar to (Block, 2000), we generalise these alignments by replacing certain function words with an associated tag. In so doing, we cluster on marker words and add flexibility to our matching process. In a post hoc stage we treat the World Wide Web as a large corpus and validate and correct instances of determiner-noun and noun-verb boundary friction. We have applied our marker-based EBMT system to different bitexts and have explored its applicability in various environments. We have developed a phrase-based EBMT system (Gough et al., 2002; Way and Gough, 2003). We show that despite the perceived low quality of on-line MT systems, our EBMT system can produce good quality translations when such systems are used to seed its memories. (Carl, 2003a; Schaler et al., 2003) suggest that EBMT is more suited to controlled translation than RBMT as it has been known to overcome the ‘knowledge acquisition bottleneck’. To this end, we developed the first controlled EBMT system (Gough and Way, 2003; Way and Gough, 2004). Given the lack of controlled bitexts, we used an on-line MT system Logomedia to translate a set of controlled English sentences, We performed experiments using controlled analysis and generation and assessed the performance of our system at each stage. We made a number of improvements to our sub-sentential alignment algorithm and following some minimal adjustments to our system, we show that our controlled EBMT system can outperform an RBMT system. We applied the Marker Hypothesis to a more scalable data set. We trained our system on 203,529 sentences extracted from a Sun Microsystems Translation Memory. We thus reduced problems of data-sparseness and limited our dependence on Logomedia. We show that scaling up data in a marker-based EBMT system improves the quality of our translations. We also report on the benefits of extracting lexical equivalences from the corpus using Mutual Information

Spelling Normalization of Historical Documents by Using a Machine Translation Approach

The lack of a spelling convention in historical documents makes their orthography to change depending on the author and the time period in which each document was written. This represents a problem for the preservation of the cultural heritage, which strives to create a digital text version of a historical document. With the aim of solving this problem, we propose three approaches—based on statistical, neural and character-based machine translation— to adapt the document’s spelling to modern standards. We tested these approaches in different scenarios, obtaining very encouraging results.The research leading to these results has received funding from the Ministerio de Economía y Competitividad (MINECO) under project CoMUN-HaT (grant agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana (grant agreement PROMETEO/2018/004)

Recycling texts: human evaluation of example-based machine translation subtitles for DVD

This project focuses on translation reusability in audiovisual contexts. Specifically, the project seeks to establish (1) whether target language subtitles produced by an EBMT system are considered intelligible and acceptable by viewers of movies on DVD, and (2)whether a relationship exists between the ‘profiles’ of corpora used to train an EBMT system, on the one hand, and viewers’ judgements of the intelligibility and acceptability of the subtitles produced by the system, on the other. The impact of other factors, namely: whether movie-viewing subjects have knowledge of the soundtrack language; subjects’ linguistic background; and subjects’ prior knowledge of the (Harry Potter) movie clips viewed; is also investigated. Corpus profiling is based on measurements (partly using corpus-analysis tools) of three characteristics of the corpora used to train the EBMT system: the number of source language repetitions they contain; the size of the corpus; and the homogeneity of the corpus (independent variables). As a quality control measure in this prospective profiling phase, we also elicit human judgements (through a combined questionnaire and interview) on the quality of the corpus data and on the reusability in new contexts of the TL subtitles. The intelligibility and acceptability of EBMT-produced subtitles (dependent variables) are, in turn, established through end-user evaluation sessions. In these sessions 44 native German-speaking subjects view short movie clips containing EBMT-generated German subtitles, and following each clip answer questions (again, through a combined questionnaire and interview) relating to the quality characteristics mentioned above. The findings of the study suggest that an increase in corpus size along with a concomitant increase in the number of source language repetitions and a decrease in corpus homogeneity, improves the readability of the EBMT-generated subtitles. It does not, however, have a significant effect on the comprehensibility, style or wellformedness of the EBMT-generated subtitles. Increasing corpus size and SL repetitions also results in a higher number of alternative TL translations in the corpus that are deemed acceptable by evaluators in the corpus profiling phase. The research also finds that subjects are more critical of subtitles when they do not understand the soundtrack language, while subjects’ linguistic background does not have a significant effect on their judgements of the quality of EBMT-generated subtitles. Prior knowledge of the Harry Potter genre, on the other hand, appears to have an effect on how viewing subjects rate the severity of observed errors in the subtitles, and on how they rate the style of subtitles, although this effect is training corpus-dependent. The introduction of repeated subtitles did not reduce the intelligibility or acceptability of the subtitles. Overall, the findings indicate that the subtitles deemed the most acceptable when evaluated in a non-AVT environment (albeit one in which rich contextual information was available) were the same as the subtitles deemed the most acceptable in an AVT environment, although richer data were gathered from the AVT environment

Automatic medical term generation for a low-resource language: translation of SNOMED CT into Basque

211 p. (eusk.) 148 p. (eng.)Tesi-lan honetan, terminoak automatikoki euskaratzeko sistemak garatu eta ebaluatu ditugu. Horretarako,SNOMED CT, terminologia kliniko zabala barnebiltzen duen ontologia hartu dugu abiapuntutzat, etaEuSnomed deritzon sistema garatu dugu horren euskaratzea kudeatzeko. EuSnomedek lau urratsekoalgoritmoa inplementatzen du terminoen euskarazko ordainak lortzeko: Lehenengo urratsak baliabidelexikalak erabiltzen ditu SNOMED CTren terminoei euskarazko ordainak zuzenean esleitzeko. Besteakbeste, Euskalterm banku terminologikoa, Zientzia eta Teknologiaren Hiztegi Entziklopedikoa, eta GizaAnatomiako Atlasa erabili ditugu. Bigarren urratserako, ingelesezko termino neoklasikoak euskaratzekoNeoTerm sistema garatu dugu. Sistema horrek, afixu neoklasikoen baliokidetzak eta transliterazio erregelakerabiltzen ditu euskarazko ordainak sortzeko. Hirugarrenerako, ingelesezko termino konplexuak euskaratzendituen KabiTerm sistema garatu dugu. KabiTermek termino konplexuetan agertzen diren habiaratutakoterminoen egiturak erabiltzen ditu euskarazko egiturak sortzeko, eta horrela termino konplexuakosatzeko. Azken urratsean, erregeletan oinarritzen den Matxin itzultzaile automatikoa osasun-zientziendomeinura egokitu dugu, MatxinMed sortuz. Horretarako Matxin domeinura egokitzeko prestatu dugu,eta besteak beste, hiztegia zabaldu diogu osasun-zientzietako testuak itzuli ahal izateko. Garatutako lauurratsak ebaluatuak izan dira metodo ezberdinak erabiliz. Alde batetik, aditu talde txiki batekin egin dugulehenengo bi urratsen ebaluazioa, eta bestetik, osasun-zientzietako euskal komunitateari esker egin dugunMedbaluatoia kanpainaren baitan azkeneko bi urratsetako sistemen ebaluazioa egin da

Integrating source-language context into log-linear models of statistical machine translation

The translation features typically used in state-of-the-art statistical machine translation (SMT) model dependencies between the source and target phrases, but not among the phrases in the source language themselves. A swathe of research has demonstrated that integrating source context modelling directly into log-linear phrase-based SMT (PB-SMT) and hierarchical PB-SMT (HPB-SMT), and can positively influence the weighting and selection of target phrases, and thus improve translation quality. In this thesis we present novel approaches to incorporate source-language contextual modelling into the state-of-the-art SMT models in order to enhance the quality of lexical selection. We investigate the effectiveness of use of a range of contextual features, including lexical features of neighbouring words, part-of-speech tags, supertags, sentence-similarity features, dependency information, and semantic roles. We explored a series of language pairs featuring typologically different languages, and examined the scalability of our research to larger amounts of training data. While our results are mixed across feature selections, language pairs, and learning curves, we observe that including contextual features of the source sentence in general produces improvements. The most significant improvements involve the integration of long-distance contextual features, such as dependency relations in combination with part-of-speech tags in Dutch-to-English subtitle translation, the combination of dependency parse and semantic role information in English-to-Dutch parliamentary debate translation, supertag features in English-to-Chinese translation, or combination of supertag and lexical features in English-to-Dutch subtitle translation. Furthermore, we investigate the applicability of our lexical contextual model in another closely related NLP problem, namely machine transliteration