The T hai Discourse Treebank: Annotating and Classifying T hai Discourse Connectives (2024)

Abstract: Discourse analysis is a highly applicable area of natural language processing. In English and other languages, resources for discourse-based tasks are widely available. Thai, however, has hitherto lacked such resources. We present the Thai Discourse Treebank, the first, large Thai corpus annotated in the style of the Penn Discourse Treebank. The resulting corpus has over 10,000 sentences and 18,000 instances of connectives in 33 different relations. We release the corpus alongside our list of 148 potentially polysemous discourse connectives with a total of 340 form-sense pairs and their classification criteria to facilitate future research. We also develop models for connective identification and classification tasks. Our best models achieve an F1 of 0.96 in the identification task and 0.46 on the sense classification task. Our results serve as benchmarks for future models for Thai discourse tasks.

## introduction discourse analysis is a challenging area of natural language processing (nlp) because the model must take into account context larger than the sentence. nlp has reached a high accuracy rate for tasks whose input is a sentence or a pair of sentences, but tasks involving larger linguistic units attract more attention from the research community as the discourse processing would enable us to improve many downstream tasks such as information extraction @xcite , text complexity assessment @xcite , and automatic essay grading @xcite . while corpora with discourse annotation exist across languages, there is a lack of such a corpus for languages with little morphology and punctuation. these languages are particularly in need of a discourse corpus because fewer formal cues are available for automated discourse-related tasks such as argument identification. for example, thanks to its morphology, sentential adverbs that signal discourse relations in english often have the -ly suffix (e.g., additionally, actually). one such language, thai, lacks both systematic descriptions of discourse elements and annotated corpora that enable supervised machine learning methods and analysis of discourse phenomena. the penn discourse treebank (pdtb) @xcite proposes a lexically grounded method for discourse annotation. each discourse relation has two arguments, which are two semantically related contiguous textual spans, and a sense, which categorizes the relationship. although is a canonical example of a discourse connective that signals a concession relation between the two arguments of the discourse relation. afterwards might signal a temporal relation. under the pdtb paradigm, a discourse relation can be explicit (signalled by a connective) or implicit (inferred). the discourse analysis in this style results in a list of discourse relations. pdtbstyle annotation has been adopted successfully in many languages such as english @xcite , hindi @xcite , czech @xcite , and chinese @xcite . following previous endeavors in these languages, we present the thai discourse treebank, a corpus of thai discourse connectives annotated on 10,000 sentences from 384 documents in the style of pdtb. all documents are annotated for explicit discourse connectives, their senses, and connectives they are paired with (if any; see section 3.2). additionally, 180 out of 384 documents are annotated for connective arguments. we then construct a thai discourse connective identification system trained on the corpus, which serves as a benchmark for the task of shallow discourse parsing and as a proof of the utility of the corpus. in addition, we provide an extensive list of the connectives and the analysis of their linguistic properties. at cursory glance, the task of annotating explicit discourse connectives may seem trivial or even amenable to automation. however, dictionary-based rules are demonstrably insufficient (see section 5.2) because many connectives are ambiguous among different senses (cf. @xcite . furthermore, there is the added layer of complexity brought about by homonymy, where words that serve as connectives in certain contexts might serve other roles such as a coordinating conjunction or relativizer in other contexts. finally, the distinctive nature of thai orthography, which lacks the punctuation that demarcate clause or sentence boundaries, further elevates the difficulty of discourse argument identification, which the pdtb-style annotation requires explicit clausal units to perform. this absence means that manual linguistically informed annotation is essential to ensure accuracy. our corpus is an initiative to address these challenges. we believe that it represents the most complete study in the context of explicit discourse connectives in a low-to-medium resource language such as thai. this focus was intentional, as we aim to study (explicit) discourse connectives as a structural element that signals coherence and discourse relations. by honing in on these connectives, we aim to contribute a dataset that not only tackles the issues highlighted but also offers insights into the discourse analysis in thai language, which can transfer to other languages where the clausal units are not clearly marked such as lao and burmese. our main contributions can be summarized as follows: ## thai discourse connectives following the original pdtb @xcite , we define discourse connectives as predicates that take two or more abstract objects as arguments, where abstract objects refer to events, states, or propositions @xcite . these arguments are called arg1 and arg2. as a convention, arg2 is identified as the argument the connective binds to. in thai, the arguments are mostly clauses. in this section, we briefly discuss thai discourse connectives and their properties, which inform our annotation guidelines for the thai discourse treebank. detailed information for each connective is provided alongside the corpus. ## discourse connective identification and classification models with our corpus, we develop classifiers for two basic shallow discourse parsing tasks: we train models with all three pdtb sense levels under two conditions, i.e., given raw tokens vs. gold-standard connectives. for each task, we experiment with three families of models. ## error analysis of models we conducted a more detailed error analysis on the predictions of our best plm model by inspecting the error rates under different sentence lengths. we also extracted 10 most common pertoken-and-misclassification errors (115 instances, total) and manually annotated each instance for its error type ( similar senses induce misclassification. we note that 32 out of the 115 errors extracted are cases of similar senses in and ; 19 instances involve a misclassification between comparison.concession.arg2-as-denier and comparison.contrast. both senses highlight a contrast between two arguments, the difference being that only the former also involves denying an implicature. (16) gives an example. ## conclusion in this paper, we present the thai discourse treebank, the first thai corpus annotated with pdtb-style discourse relations. we develop the annotation guidelines for thai and annotate 18,374 instances of discourse connectives over 10,602 sentences on top of the lst20 corpus. we benchmark the task of connective identification and sense classification using log-linear models and plm-based systems. we also provide extensive qualitative analyses of the data and model results. in later releases of the data, we hope to incorporate syntactic information and implicit connectives.