Linking discourse-level information and the induction of bilingual discourse connective lexicons

1. Introduction

Representing linguistic content in the form of linked data has recently become an active area of research in the field of Natural Language Processing. There has been a growing interest for linked data models and applications, leading to knowledge graphs, wordnets, and dictionaries, to name a few. Following the TextLink project,1

A collaborative effort that evolved during the lifespan of the TextLink project, TED-Multilingual Discourse Bank (TED-MDB) is a corpus annotated for discourse relations of English TED talks and translations into multiple languages (European Portuguese, Lithuanian, German, Russian, Polish, and Turkish). Since most of the languages involved in the project did not have a discourse connective lexicon when this resource was created, no discourse connective lexicons were utilized during its development. The teams took the members of syntactic classes such as subordinating and coordinating conjunctions and adverbials as a starting point to determine the set of discourse connectives in each language. Each team was also allowed to specify discourse connectives that go beyond the syntactic classes.

TED-MDB offers an ideal domain for carrying out cross-lingual discourse analysis and inducing monolingual and bilingual discourse connective lexicons for a new set of languages. But this resource presents a challenge for both aims because discourse relations in one language are annotated blind to the annotations performed on other languages in order to accurately account for the differences exhibited across languages. Such natural cross-lingual discrepancies could hinder any efforts of cross-linguistic comparisons or induction of new resources such as bilingual lexicons among the languages included in the corpus. A sentence-to-sentence alignment of the texts would not suffice for the induction of lexicons, neither would it enable cross-linguistic analysis, as one would not make sense of which connectives are kept or omitted, and how the meaning of a connective varies. A data linking task, more specifically, a relation linking task2

The main contributions of the paper are: (1) to introduce two alternative methods for relation linking in TED-MDB, one relying on traditional word alignments and the other one employing multilingual sentence embeddings. To the best of our knowledge, the latter method has neither been investigated for the relation linking of a multilingual discourse corpus, nor for the languages under consideration in the present work; (2) to present a newer version of TED-MDB with the linked labels over each text in the corpus, thus enhancing the data structure of the corpus; (3) to present an overview of the discourse structures across TED-MDB languages facilitated by the relation linking task, and (4) to automatically induce new bilingual discourse connective lexicons for each TED-MDB language (target languages) and English (source language), substantially increasing the number of available discourse connective bilingual lexicons.3

The rest of the paper proceeds as follows: in the next section (Section 2), the main data source, TED-MDB is summarized. In Section 3, a short review on existing bilingual and multilingual discourse connective lexicons is provided. Section 4 describes the data linking task, highlighting its challenges in Section 4.1, followed by the details of the two proposed methods in Section 4.2 and Section 4.3. Section 5 provides an evaluation of the linked data and an error analysis, also introducing the structure of the resulting data in XML format (Section 5.3). In Section 6, an overview of the discourse structures observed in TED-MDB is presented together with the statistics obtained from the relation linking task. In Section 7, the bilingual lexicons that link the connectives induced from the linked data are described. The paper ends with a conclusion and some future directions for further research (Section 8).

TED talks are prepared presentations given in English to a live audience. The audio/video recordings are made available online together with English subtitles in a large set of languages, which are translated by volunteers and checked by experts. The subtitles ignore most dysfluencies, such as hesitations and filled pauses, although pragmatic discourse makers, such as well, are usually retained [18]. The wide coverage of TED talks in terms of topics and translated languages make them an ideal source of data for parallel corpora and contrastive studies on a spoken genre.

The raw texts annotated in TED-MDB consist of English transcripts, and their translations into six different languages. The talks are presented by native English speakers and cover different themes as listed in Table 1.

Annotation Scheme of TED-MDB: The texts included in TED-MDB are annotated in the Penn Discourse TreeBank (PDTB) style [28], where discourse relations that hold between two arguments of a discourse connective (Arg1 and Arg2) are identified. Discourse relations are referred to as Explicit relations when marked by a discourse connective, Implicit when no overt connective is present.

In an Explicit discourse relation, the relation that holds between the two arguments are made salient by the connective, as in Example 1.4

In the absence of a discourse connective, the relation is inferred from the context and the annotator inserts a connective (referred to as the ‘implicit connective’) that would make the inferred relation explicit, as in Example 2.

When a relation is inferred but an explicit connective is missing, it is sometimes not possible to insert an implicit connective since this would lead to redundancy of expression. There is often another expression that does the job. In those cases, the relation is annotated as Alternative Lexicalization, or AltLex (Example 3).5

Discourse relations of the type Explicit, Implicit and AltLex are labelled with a sense chosen from the PDTB 3.0 hierarchy, such as Contingency:Cause:Result [39]. The format of the sense tags is such that, the first sense is referred to as the top-level or Level1 sense (e.g. Contingency), that is, the highest semantic category in the tagset of senses. The second level sense, or Level2 sense (Cause) refines the Level1 sense. Level3 senses (e.g. Result) further characterize the contribution of each argument. In this way, the sense tags provide information about the full semantics of the relation. The complete sense hierarchy is provided in Fig. 1.

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Fig. 1.

PDTB 3.0 sense hierarchy [39].

Relations can also hold between entities, where one of the arguments provides additional information about the entity introduced in the other argument. These cases are annotated as an Entity Relation (EntRel), as illustrated in Example 4. Finally, when no relation holds between the two adjacent sentences, the relation is of the type NoRel (Example 5). The annotation scheme is summarized in Table 2.6

Additionally, a new top-level sense called Hypophora was introduced in TED-MDB, which applies in cases where the speaker asks a question and immediately answers it. In monologues, not all questions are asked to be answered: in TED Talk transcripts, Hypophora has the purpose of creating dialogism and making the presentation livelier (Example 6).

During the annotation phase, the texts in each language were annotated simultaneously but independently of the original English texts to ensure that annotations capture the discourse structure of each translated language as independently as possible. This design criterion led to different sets of relations annotated for each language. Table 3 provides the number and the percentage of each type of relation (Explicit, Implicit, AltLex, EntRel and NoRel) in each language.

In order to test the reliability of the annotations, ~20% of the whole corpus (i.e. two TED talks per language) are annotated by an independent annotator, using the annotation scheme and following the annotation principles summarized above. The inter-annotator agreement (IAA) is performed on two levels following [21]: (i) whether or not annotators spotted a relation between the same discourse units, (ii) whether or not the spotted relation is of the same kind (type and sense-wise). The agreement on relation spotting is measured via F-score, whereas the type and sense agreement on the spotted relations is measured via simple ratio agreement and Cohen’s Kappa. The IAA on both levels is found to be at a good standard ( > 70 ).

The last decade has seen an upsurge in the development of monolingual discourse connective lexicons, such as LexConn [30], LiCo [11], DiMLex [31], CzeDLex [22], LDM-PT [17], and TCL [41], all included in the Connective-Lex database, designed to act as a hub for publicly available monolingual connective lexicons. In the back-end, the lexicons are stored in XML format with a simple structure; hence, the integration of a new lexicon in the database is a straightforward process. Currently, the database hosts lexicons of 11 different languages. Researchers have envisioned linking the existing lexicons but the linking task poses certain challenges as they may be modeled on different relation taxonomies [34], or the existing connective inventories may not be machine-readable as is the case for many of the discourse connective inventories not integrated into Connective-Lex. The connective inventories may also vary in depth and detail making the mapping of linguistic descriptions difficult. For example, the Spanish Diccionario de partículas discursivas del español (DPDE – [6]) includes explicit information on discourse particles in Spanish but it excludes conjunctions and prepositions. The German resource Handbuch der Konnektoren [5,26] contains detailed linguistic descriptions of discourse connectives, including their possible positions in a sentence, also the register and possible modifiers.

Nevertheless, there exist a few multilingual discourse connective lexicons [3,27], and a recent work [9] achieves the first comprehensive edition of machine-readable discourse marker lexicons developed in accordance with web standards and Linguistic Linked Open Data (LLOD) principles. This work links the sense definitions and annotation schemes in Connective-Lex along with other existing discourse marker inventories such as TED-MDB, under a common standard.

The main task of the present work, relation linking, involves linking the components of a relation in different languages, i.e. the labels for relation type, arguments, connectives, and their discourse senses (if available), as explained in Section 2. The objective, therefore, is not aligning the words and sentences of different languages, but linking the connectives, sentences or parts of sentences annotated as a relation together with the linguistic labels over those relations. The outcome of this work will enable access to the discourse labels over texts in different languages on the level of format, as well as permitting easy access to the discourse structures of different languages by means of the reference to existing labels [9]. Furthermore, the main task of the current paper will not only support the induction of a multilingual discourse connective lexicon, but it will also allow immediate access to different datasets within TED-MDB. In the sections that follow, we present the steps of a novel approach of linking texts and the labels over those texts, and we describe the current format of the resulting resource. However, it must be noted that Linked Open Data Principles [8] are yet to be applied to this resource.

The relation linking task can be seen as a variant of the annotation projection task, where the aim is to transfer (manually or automatically), the annotated discourse relations in one language to another through parallel corpora [15,16,38]. Yet, despite certain similarities, they noticeably differ from each other, because in annotation projection, the linguistic information is available only for one language. Hence, being completely clueless about the target language, the projection method can be deemed successful to the extent that the projected relations mimic the original ones and cannot be punished for missing the discourse relations on the target side. However, in our case, the linguistic information is available for both sides and instead of an uninformed projection of the source text discourse relations, one should decide if there is a corresponding discourse relation on the target text, which is not a straightforward task. In the rest of this section, the challenges surrounding the task undertaken and the methods to address these challenges are provided.

4.3. Method II: Relation linking through cross-lingual sentence embeddings

The second method uses the modern, language agnostic sentence encoders which are capable of assigning similar representations to semantically similar linguistic units across languages. The method is a continuation of a previous study [24] which performed relation linking only for the English-Turkish pair in TED-MDB. It starts with a pre-processing step which is similar to that of the first method, i.e., the raw texts are sentence-tokenized and aligned in the manner already described. For relation linking, the relations in each bitext unit10

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A bitext unit is a pair of source and target sentences which have either partial or full translation equivalence [35].

are paired constructing relation matrices. Then, for all pairs with a semantic similarity over a certain threshold learned in the training phrase (Section 4.3.1), a composite score is calculated. This score not only reflects the agreement on all three sense levels and the relation type of the matched pair (if they have no match in their Level1 sense, relation type match is discarded), but also the semantic similarity between the text segments (Arg1 + connective (if available) + Arg2). Semantic similarity is calculated as the cosine similarity between the LASER embeddings [1] of each relation’s text segments.

4.3.1. Adjusting the semantic similarity threshold

Unlike the first method, the second method involves a training phase, namely, the learning of a semantic threshold parameter. To learn this parameter, training is performed for language pairs involving the source language and three of the target languages in TED-MDB, namely, Turkish, Portuguese, and Lithuanian (EN-TR, EN-PT, EN-LT). First, the relation labels over English texts are automatically matched with those on the texts of these three languages. Then, the performance of the automatic process is manually checked by the authors and wrong matches are corrected. In the training phase, further performance evaluation is done using this manually checked data. Throughout the paper, we refer to this data as manually-corrected or semi-automatically linked data.

For training, the six English files are split into the train and test data sets considering the overall relation counts in the English texts. As the data size is low, to eliminate over fitting, the data is evenly split into train and test sets.11

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The exact relation-wise train:test data ratio is 52:48.

Also, to have a representative training set, four talks are set aside as the training set leaving the other two to be used as the test set.12

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Specifically, talks with ids of 1971, 1978, 2009 and 2150 are used as the training data.

Using semantic threshold values starting from 0 to 0.95 and increasing by 0.05, the algorithm is repeated. The optimum threshold value that yields the best F-score on average for all three language pairs is selected and validated in the test set, and later applied to other language pairs.

Figure 2 shows the effect of the semantic threshold on the relation linking performance according to the evaluation metrics. For better readability, the figures start from 0.35. However, the performance is found to be stable between 0 and 0.55 across languages. The effect of the threshold starts to become visible around 0.6 for all languages. Even though maximum performance is observed between the ranges 0.6–0.7 for Portuguese and 0.65–0.75 for Lithuanian, the performance after the 0.65 threshold shows a rapid decrease for Turkish. Between the threshold values 0 to 0.55, the F-score is 0.82 on average for Lithuanian and 0.88 for other language pairs. However, keeping the parameter in the 0–0.55 range causes False Positives to increase. Due to no or little control of this parameter, the model relies on the similarity of two relations only at the sense levels and relation types. This reliance often results in linking English relations with wrong target relations. This can be seen in Example 9, where the relation anchored by and in the English sentence is falsely linked to that of ve ‘and’ in Turkish, as their senses and relation types match. However, different from the English relation, the Turkish relation holds between thinking about how to create maps and how to draw them.

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Fig. 2.

The change of evaluation metrics (Accuracy, Precision, Recall and F-Score) at different levels of semantic threshold values. Although the threshold is searched between 0 and 0.95 at increments of 0.05, to achieve better visualization, only the values between 0.3 and 0.85 are provided.

When we think about mapping cities, we tend to think about roads and streets and buildings, and the settlement narrative that led to their creation, or you might think about the bold vision of an urban designer, but there’s other ways to think about mapping citiesandhow they got to be made. [Explicit, Expansion:Conjunction] (English, TED Talk no. 2150)

Şehirlerin haritalarını oluşturmayı düşündüğü-müzde yollar, sokaklar, caddeler, binalar ve şehirlerin oluşumuna yol açan yerleşim hikayeleri aklımıza gelir. Ya da bir kentsel tasarımcının cesur vizyonunu düşünebilirsiniz. Ancak, şehirlerin haritalarını oluşturmayı düşünmeninveyapmanın başka yolları da var. [Explicit, Expansion:Conjunction] (Turkish, TED Talk no. 2150)

4.3.2. Relation linking

In the relation linking stage, a scoring algorithm is developed, where the links over the relations are determined on the basis of the total score obtained from the semantic similarity between the relations, the degree of correspondence for sense levels, and the relation types. Each step is described below.

The similarity score between the relation pairs are calculated considering their text segments. The pairs that do not exceed the similarity threshold (0.65) learned in the previous step (described in Section 4.3.1) are discarded.

The semantic similarity score is combined with another score that reflects the semantic match between the relation pairs in each source-target language pair. That is, in a ranked manner, a match on Level1 sense is given a score of 1000, a match on Level2 sense 100, a match of Level3 sense 10, and 1 is assigned for relation type match (Explicit, Implicit etc.). If there is no match on the Level1 sense, scoring is not done for other sense levels and relation realization types, but especially for NoRels and EntRels, relation realization type match plays a key role as they are not assigned a sense label.

For each source relation, the target relation that yields the highest score is marked as its linked pair and the same procedure is repeatedly applied until no relation pair is left in the matrices.

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Table 6

The Relation Scoring Matrix for Example 10. The numbers refer to the scores based on sense/type agreement + semantic similarity of the segments (Arg1 + Conn (if available) + Arg2)

	Ama	kadar	ve
But	1111 + 0.85	1001 + 0.79	0 + 0.72
and	0 + 0.69	0 + 0.71	1111 + 0.75
as	1001 +0.8	1111 + 0.85	0 + 0.77

The whole procedure is illustrated on a sample text provided in Example 10. The text involves three Explicit relations in two languages (EN, TR) signaled by (but, as, and) and (ama ‘but’, kadar ‘as’, ve ‘and’) as summarized in Example 11. As the first step, all pairwise combinations of these relations are calculated, resulting in a (3x3) matrix as shown in Table 6. Then, following the scoring procedure, each pair is assigned a score. On the Turkish side, the connective label Ama (the first column of Table 6) matches the English connective label But in relation realization type and relation sense in all levels; similarly, the connective label kadar in Turkish matches the English connective label as in relation realization type and relation sense in all levels. The same situation holds for the third Turkish-English connective label pair ve and and. For a non-linking case, consider the third English relation conveyed by and, which has no match with the first Turkish connective Ama at any sense levels. For this reason, relation realization type match is not considered between Ama and and. Then, each source relation (i.e. each row) is linked to the target relation (i.e. each column) that has the maximum score

Years have passed, but many of the adventures I fantasized about as a child – traveling and weaving my way between worlds other than my own – have become realities through my work as a documentary photographer. But no other experience has felt as true to my childhood dreams as living amongst and documenting the lives of fellow wanderers across the United States. (English, TED Talk no. 2009)

Yıllar geçti, ama çocuk olarak hayalini kurduğum birçok macera – benim dünyam dışındaki dünyalar arasında seyahat ederken ve yoluma dokunurken – bir belgesel fotorafçısı olarak işim aracıyla bunlar gerçek oldu. Ama hiçbir başka deneyim çocukluk rüyalarımı yaşayanlar arasında olmak kadarve Birleşik Devlet boyunca gezgin arkadaşların arasında yaşamak kadar gerçek hissettirmedi. (Turkish, TED Talk no. 2009)

English :

DR13

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DR stands for Discourse Relation.

-Explicit-Comparison.Concession.Arg2-as-denier-DC14

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DC is used for Discourse Connective.

-But

DR-Explicit-Comparison.Similarity-DC-as

DR-Explicit-Expansion.Conjunction-DC-and

Turkish:

DR-Explicit-Comparison.Concession.Arg2-as-denier-DC-Ama

DR-Explicit-Comparison.Similarity-DC-kadar

DR-Explicit-Expansion.Conjunction-DC-ve

The examination of the results from our relation linking procedure revealed the need for certain revisions. As mentioned before, it is common for more than one discourse relation to hold between similar arguments ([29]), which could lead to false relation linking if only the arguments are linked. So, in addition to the similarity between argument spans, the semantic similarity between discourse connectives is also checked. Second, an AltLex in one language may be converted into an Explicit discourse relation in another language. The linking algorithm is unable to cover such cases as it works on sentence-aligned bitext units. In order to eliminate this pitfall, if a relation is not matched with a relation in the target language in its parallel unit, it is evaluated once more in the succeeding alignment unit.

In the literature, data linking quality is evaluated by using the standard precision, recall and F-score metrics. Precision is the positive predictive value or the proportion of the assigned links that are true matches (also known as true positives). Sensitivity or recall is the proportion of the true matches that are correctly identified, and finally, accuracy is the proportion of the valid matches and non-matches that are correctly identified. F-score represents the performance of the method and it is the harmonic mean of precision and recall [10,21,29].

Data linking quality is dependent on the task domain and there is always a trade-off between precision and recall. Usually, when the number of non-linking points is large in the sorce and target data sets, accuracy is not considered as a good measure. However, as the task at hand is relation linking, accuracy should also be taken into consideration; providing information on the non-linking relations is as important as providing linking data. In a data linking task such as ours, non-linking relations offers valuable insights into linguistics, machine translation and in particular, into the assessment of the annotation quality.

5.3. Dissemination of the linked relations

In order to facilitate further research, the resulting set of linked and non-linked relations for each language pair (English-Language X) are made publicly available as a set of XML files.16

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https://github.com/MurathanKurfali/Ted-MDB-Annotations

A sample English-Turkish file is provided in Fig. 3 in order to illustrate the underlying structure. The relations are stored under the <relation> nodes with children of Arg1, Arg2 and Conn (if there is any), and five attributes that encode the sense, type information of the relation as well as the meta-level information regarding its language, its source TED talk ID, and the unique relation ID. The linked relations are combined under the <relation_pair> elements which further encode the alignment score, showing the confidence of Method II in linking the relations. The set of linked relations are grouped under the <linked_relations> element. Additionally, the set of non-linked relations, which do not have any counterpart in the other language, are listed under the <non_paired_relations>. We believe that these relations are equally informative regarding the discourse structures of the languages involved. It must also be noted that these links are created automatically so there may be some false negatives in this set which can be corrected via manual effort.

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Fig. 3.

A sample file showing the structure of the adopted XML schema in the published linked relations.

Parallel corpora have enabled a leap ahead in cross-linguistic investigations and in translation studies. Yet, due to the scarcity of parallel corpora annotated for discourse relations on both sides, previous cross-lingual work has largely been confined to a specific aspect of discourse, e.g. omission of discourse markers [12,44], mostly using parallel data with manual annotations on only one side. However, thanks to the availability of discourse information on both ends and the relation linking carried out in this work, TED-MDB enables studying the discourse of English and the translated texts in a comprehensive manner. To this end, in the rest of the section, a general overview of how the discourse structure of English and the target languages differ is outlined concentrating on two questions: (i) Do discourse relations exhibit differences in how they are realized (e.g. explicitly or implicitly) in different languages? (ii) How do the semantics of the relations that hold between the same text spans change cross-lingually? To answer these questions, we use the linked relations. In order to maximize the reliability of our analysis, we used the manually-corrected data which exists for three target languages (Lithuanian, Turkish, Portuguese) and the automatically linked data obtained through Method II for the remaining languages. Therefore, the observations should be approached cautiously due to possible incorrect links; yet, high F-scores are obtained in capturing the semi-automatically prepared links (see Table 7). This suggests that the reported results closely follow the distribution in semi-automatically linked data.

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Fig. 4.

Heatmap visualizations of the confusion matrices for relation type of the linked discourse relations. Rows correspond to the English relations and columns denote target languages. The matrices are normalized row-wise where each cell denotes the percentage of English relations converted to the respective label in the target language. Confusion matrices created from manually-corrected links are highlighted in bold.

The following analysis is mainly confined with the descriptive analysis of the aforementioned points, leaving an in-depth linguistic investigation for future work.

Cross-lingual variation in relation realization types: In order to answer the first question, the relation types of each linked relation are compared with each other in a pair-wise manner. Figure 4 shows the heat-map visualizations of the row-wise normalized confusion matrices for relations in all language pairs. The rows represent the relations in English, where each cell shows how often English relations are realized as the respective label on the X-axis. (e.g. the second cell of the first row of Fig. 4(a) reads “15% of English explicit discourse relations are realized implicitly in German.”) Colors represent the density of agreements, where lighter colors visualize low agreement, getting redder as the agreement increases (a more detailed breakdown of the color-coding is provided in each figure). In a perfect match, only the diagonal cells would be red with the off-diagonal cells being complete white/gray.

According to Figures 4(a) to 4(f), the target relations vary greatly with respect to English relations in terms of their types. On average, 487 of the English relations are linked to the relations in each target language, and only 72% of them retained their type. Of the five relation types, the Explicit discourse relations (79%) and Implicit discourse relations (78%) are conserved most frequently, whereas 60.1% of the AltLexes are converted into other relation types. The language-specific breakdown of these variations are presented in Figs 4(a) to 4(f).

When all language pairs are considered, the top three conversions (from English to the target languages) are as follows: 31.92% of AltLexes become Explicit; 29% of EntRels become Implicit and 16% of Explicit relations become Implicit. Explicit relations becoming Implicit and AltLexes becoming Explicit can be linguistically motivated and arise due to language-specific properties, the translator’s choice, or both. The EntRel cases are often methodologically motivated: the annotator in one language decides to label a relation as EntRel while another annotator as Implicit.

Moreover, EntRels and Implicit relations have been reported to be the most easily confused pairs even within the same language [42] as their distinction is very subtle. These two relations are semantically related to the extent that EntRels are exploited as Implicit Expansion discourse relations to increase the available training data in implicit discourse relation recognition tasks, yielding increases in overall performance [13,25]. In fact, we see the same tendency in our corpus, where on average, 78.86% of English EntRels that become Implicit are annotated as Implicit Expansion relations in the non-English language, in accordance with the previous observations.

Finally, implicitation of a discourse connective (the omission of a connective in the target text where there is one in the source text) is found to be the third common shift (or the second one, if EntRel to Implicit conversions are dismissed as being reasonably interchangeable) in relation types. Given that implicitation of discourse connectives is an actively studied topic in discourse [45], the results of the current work can be used safely in future crosslinguistic investigations of implicitation (or its reverse, expliciation) of discourse connectives. In all language pairs in TED-MDB, at least 10% of the English relations are found to be realized implicitly. These results raise a further question: are all explicit discourse relations equally likely to be realized implicitly in the target language? Interestingly, implicitation dominantly occurs with Expansion discourse relations (Table 8). The same is not true for explicitation, where Contingency discourse relations are relatively more often explicitated than others on average, but they are far from being as dominant as the implicitated Expansion discourse relations.

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Fig. 5.

Heatmap visualizations of the confusion matrices for the sense of linked discourse relations. Rows correspond to the English relations and columns denote target languages. The matrices are normalized row-wise, where each cell denotes the percentage of English relations converted to the respective label in the target language. Confusion matrices created from manually-corrected links are highlighted in bold.

Cross-lingual variation in discourse sense: Unlike relation types, the discourse sense of the connectives are found to be quite stable across languages. On average, 86.84% of English discourse relations retained their top-level sense in the target languages.17

In addition to enabling linguistic investigations of cross-lingual discourse structures, a parallel corpus with linked relations has a number of practical use cases, where building bilingual discourse connective lexicons is one of them. Bilingual discourse connective lexicons document the relationships between discourse connectives over two languages. Yet, the existing discourse connective lexicons are overwhelmingly monolingual, where [3,9,14,27] are the only notable exceptions. In order to increase the breadth of the existing resources, the present paper exploits relation links to build such lexicons for each English-Language X pair. In the rest of the paper, the TED-MDB lexicons are introduced including our motivation to create them and their extraction procedure. Their coverage and limitations are also discussed.

7.2. Procedure

One way of compiling a bilingual lexicon involves interlinking existing monolingual discourse connective lexicons by exploiting translation candidate tables calculated from large parallel corpora. To arrive at the bilingual discourse connective lexicon, the translation candidates are filtered in a way that for each possible sense of the source language connective, only those translations that can signal the same sense (determined by the discourse connective lexicons of those particular languages) are kept [27]. Instead, in the current study, a more direct approach is adopted by exploiting the linked discourse connectives. This alleviates the need for other resources. The procedure mimics the extraction of monolingual lexicons from an annotated corpus, closely following [14]. Using the relation links, discourse connectives in different languages are mapped with one another, provided that they exist in a linked relation which conveys the same sense. The rationale behind our procedure is that bilingual discourse connective lexicons compiled from resources where their contexts and usages are annotated (e.g. in the form of discourse relations) readily have access to such discourse-level information regarding connectives and can capture the complex mappings between them across languages.

The selection of discourse connectives and the languages solely rely on the TED-MDB annotations.19

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which is the only resource for most of those languages.

The extraction of bilingual discourse connective lexicons from the linked relations is straightforward as the more burdensome issues such as deciding which lexical items serve as discourse connectives or which sense they convey in a particular context have already been handled and implemented on the annotations. One limitation of working with TED-MDB is its size, which amounts to 278 Explicit relations on average considering all the data set (Table 3). To remedy this situation and extend the coverage of the lexicons, implicit discourse connectives are also included 20

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AltLex relations are not included.

, as in [14]. Specifically, the method consists of two steps, preceded by pre-processing:

In the pre-processing step, all linked relations that include a non-Explicit or a non-Implicit discourse relation on either side, as well as those mapping relations that are not annotated with exactly the same sense are filtered out.

For each connective in the source language, the list of its possible senses is compiled.

For each observed sense of each discourse connective in the source language, translation equivalents are searched among the target language annotations using the linked relations. Therefore, connective translations are provided (if any) separately for each sense. However, it is not uncommon for a matched discourse connective pair to be polysemous between the same set of senses (e.g. the in fact/na verdade pair is found to signal both Expansion:Instantiation and Expansion:Level-of-detail:Arg2-as-detail in English and Portuguese, respectively), so sometimes, the same translations re-appear under different senses.

This procedure is applied in both directions for each language pair (of the form English-Language X). Again, the linked relations obtained through Method II are used in the compilation of the lexicons.

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Fig. 6.

A screenshot showing the entry for “böylece” in the Turkish-English lexicon.

In the current work, in order to improve the data structure of TED-MDB and facilitate further research, two methods for data linking (more specifically, relation linking) are proposed, one of them using word alignments and the other relying on distributional semantics. The data linking task attempted in the current work resulted in a resource with a better and more informative data structure than the original TED-MDB and enabled the induction of bilingual lexicons of six language pairs, which are presented in a user-friendly format, where discourse connectives, both Explicit and Implicit, are linked. The new resource will facilitate cross-lingual investigations of the discourse structures of the languages included in the corpus, as a preliminary examination already illustrates. Due to the challenges specific to the current task, each relation linking method was tailored to the current context through a set of heuristics. Overall, the second method, which employs multilingual embeddings to relation linking, is favored over the more traditional first method due to its higher performance. The second method is also preferable because it avoids the need for a large parallel corpus, which may not be available for most of the language pairs.

The present paper applied the data linking concept to a different area of research, that is, to the cross-lingual linking of relation annotations, which has its unique challenges. This leads to two promising results: First, a multilingual corpus with the linked relations would enable many cross-linguistic studies to be performed, including machine translation, shallow discourse parsing. Secondly, the bilingual discourse connective lexicons were extracted purely contextually. These lexicons can be useful in many domains of information technology.

In the future, we plan to extend the bilingual lexicons to the multilingual level to enable a better perspective on the use of discourse connectives across multiple languages. We also intend to integrate the dictionaries into the linked discourse connective lexicon of [7], offering new functionalities such as cross-lingual querying over the linked relations. In this way, a resource in machine-readable format compatible with the Linked Open Data Principles will be obtained.