TED - MDB Lexicons: Tr- E n C onn L ex, Pt- E n C onn L ex (2020)

Abstract: In this work, we present two new bilingual discourse connective lexicons, namely, for Turkish-English and European Portuguese-English created automatically using the existing discourse relation-aligned TED-MDB corpus. In their current form, the Pt-En lexicon includes 95 entries, whereas the Tr-En lexicon contains 133 entries. The lexicons constitute the first step of a larger project of developing a multilingual discourse connective lexicon.

## introduction during the past decade or so the interest in discourse studies have dramatically increased following the release of the pdtb 2.0 corpus @xcite and, later, with the textlink initiative 1 . in parallel to this interest, available resources annotated for various discourse-level phenomena have expanded, where discourse relational devices (drds) have received a special interest leading to the connective-lex database @xcite . connlex is a joint online database project, which is the first attempt to bring together connective lexicons of different languages. it currently hosts the connective lexicons of nine different languages providing a web-based interface together with a crosslinguistically applicable xml schema. the entries in the lexicons provide fundamental information about discourse connectives, such as orthography, syntactic category, and their senses. the connlex project pursues the aim of expanding the database both in coverage (by adding new languages) and depth of the information. however, except for a few resources, most of the previous effort on devising discourse connective lexicons has relied on monolingual resources and any multilingual links that were provided have not gone beyond offering english equivalents. few exceptions involve the bilingual italian-german contrastive/concessive connective lexicon based on the cross-lingual projection of monolingual lexicons for italian and german @xcite , and the very recent geczlex, anaphoric connective lexicon for czech and german @xcite . the main contributions of the present study are (1) proposing an alternative way of producing bilingual lexicons, potentially applicable to building multilingual lexicons, (2) providing new bilingual discourse connective lexicons for european portuguese-english and turkish-english by (3) considering not only the explicit discourse relations but also the implicit relations in a recent multilingual discourse bank, namely ted-multilingual discourse bank (ted-mdb) annotated in the pdtb style @xcite . the lexicon entries are extracted from ted-mdb, where each relation in the source language is aligned to its semantic equivalent in the target languages (turkish and european portuguese) (see §2.1). in their current form, the pt-en lexicon includes 95 entries covering 51 connectives in portuguese and 57 connectives in english, while the tr-en lexicon contains 133 entries with 72 connectives in turkish and 56 in english. the rest of the study is structured as follows: we firstly summarize the main data source, ted-mdb followed by the discourse relation alignment procedure ( §2), the output of which is used as inputs to construct bilingual lexicons. §3 describes the construction of the bilingual lexicons in detail. in §4, we discuss issues concerning our lexicon construction procedure. §5 concludes the paper presenting some future directions. ## ted-mdb ted-mdb is a resource of ted talk transcripts comprising 7 languages manually annotated for discourse relations. it includes english, the source language (sl) along with transcribed texts in ger-man, lithuanian, european portuguese, russian, turkish and polish (target languages, or tls). 2 following the rules and principles of the pdtb, it annotates five discourse relations types (henceforth, drs) with respect to the pdtb-3 sense hierarchy @xcite and ultimately aims to provide a clearly described level of discourse structure and semantics in multiple languages, thus engendering discourse parsing studies in multiple languages. ted-mdb currently involves 6 ted talk transcripts annotated with 5 dr types (explicit, implicit, altlex, entrel, norel), their senses and binary arguments, amounting to a total of 3649 tokens. the annotations have been carried out by native speaker annotators of the languages involved using the pdtb annotation tool. (lee et al., 2016) 3 this tool stores the dr annotations in separate pipe-delimited files. ## ted-mdb lexicons as shown in @xcite and @xcite , preparing a bilingual lexicon of discourse connectives is not a straightforward task requiring a variety of resources to compute a translation candidate table including monolingual dc lexicons of the tls and a large parallel corpus (with at least 2m parallel sentences). a monolingual discourse connective lexicon exists for portuguese @xcite and one is being developed for turkish @xcite but parallel corpora of the required size are absent for the language pairs under investigation. thus, the current study is built on the observation that just as monolingual lexicons can be compiled from annotated resources, bilingual dictionaries of discourse connectives can be constructed from a similar though low scaled parallel corpus such as ted-mdb. this corpus includes 375 bi-sentence units for english-turkish and 364 for english-european portuguese. the rest of the section describes the method employed to create two such bilingual dc lexicons of english-turkish and english-european portuguese. ## discussion to the best of our knowledge, the ted-mdb lexicons presented here constitute the first attempt to construct a bilingual connective lexicon directly from an annotated parallel resource. compilation of bilingual lexicons in this way has a number of practical benefits, where the main advantage is being not dependent on external resources. it alleviates the need for parallel corpora required to extract the translation candidates to map the connectives in different languages onto each other and does not necessitate monolingual dc lexicons, a challenging and time consuming effort especially when started from scratch @xcite . also, as all entries are populated from an annotated corpus, the lexicons are guaranteed to be symmetrical, and the bilingual examples provide an opportunity to observe the usage of connectives in context in two languages. it must also be noted that despite being compiled from a set of merely 300+ relations in each language set, our bilingual lexicons roughly account for 30% of the documented connectives of these languages; hence, their coverage is more impressive than it looks ( finally, as the manual control of the dr alignments constitutes one of the two non-automatic steps of our pipeline, we investigated its effect on the final lexicons to guide future research. to our surprise, the automatic alignment procedure turns out to be more than satisfactory: we were able to fetch more than 96% of all entries in the gold lexicons, suggesting that even a multilingual lexicon involving all languages in ted-mdb can be automatically constructed. this is left for future work. ## conclusion in translation, the choice of a dc that best conveys the sense of a relation and renders the relation in a natural way is not a trivial task. at a minimum, it requires careful consideration of the multiple senses of the connectives and their parts-of-speech. even a bilingual dictionary is not always helpful for a translator. bilingual lexicons built on the basis of naturalistic data is important to aid both machine and human translation as well as second language learners. in this study, we described a method of building two bilingual lexicons using aligned dr annotations. both lexicons are available online as html web pages. 6 in contrast to previous bilingual lexicon studies, we did not use monolingual connective lexicons or dictionaries as the former was absent (at least for turkish), and the latter caused loss of useful data. although the alignment and the lexicon extraction procedures have been applied to two languages so far, this approach has the potential to be extended to other language pairs covered in the ted-mdb corpus, and this is what we plan to do as a future study.