Malta

This paper introduces an approach to stress identification in Tamil and Telugu, leveraging traditional machine learning models—Fasttext for Tamil and Naive Bayes for Telugu—yielding commendable results. The study highlights the scarcity of annotated data and recognizes limitations in phonetic features relevant to these languages, impacting precise information extraction. Our models achieved a macro F1 score of 0.77 for Tamil and 0.72 for Telugu with Fasttext and Naive Bayes, respectively. While the Telugu model secured the second rank in shared tasks, ongoing research is crucial to unlocking the full potential of stress identification in these languages, necessitating the exploration of additional features and advanced techniques specified in the discussions and limitations section.

EACL 2024

Pinealai_StressIdent_LT-EDI@EACL2024: Minimal Configurations for Stress Identification in Tamil and Telugu

dravidian language

fasttext

naive bayes

emotions

machine learning

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

Classifying discourse relations is known as a hard task, relying on complex indices. On the other hand, discourse-annotated data is scarce, especially for languages other than English: many corpora, of limited size, exist for several languages but the domain is split between different theoretical frameworks that have a huge impact on the nature of the textual spans to be linked, and the label set used. Moreover, each annotation project implements modifications compared to the theoretical background and other projects. These discrepancies hinder the development of systems taking advantage of all the available data to tackle data sparsity and work on transfer between languages is very limited, almost nonexistent between frameworks, while it could improve our understanding of some theoretical aspects and enhance many applications. In this paper, we propose the first experiments on zero-shot learning for discourse relation classification and investigate several paths in the way source data can be combined, either based on languages, frameworks, or similarity measures. We demonstrate how difficult transfer is for the task at hand, and that the most impactful factor is label set divergence, where the notion of underlying framework possibly conceals crucial disagreements.

Zero-shot learning for multilingual discourse relation classification

Although diagrams are fundamental to Rhetorical Structure Theory, their interpretation has received little in-depth exploration. This paper presents an algorithmic approach to accessing the meaning of these diagrams. Three algorithms are presented. The first of these, called reenactment, recreates the abstract process whereby structures are created, following the dynamic of coherence development, starting from simple relational propositions, and combing these to form complex expressions which are in turn integrated to define the comprehensive discourse organization. The second algorithm, called composition, implements Marcu's strong nuclearity assumption. It uses a simple inference mechanism to demonstrate the reducibility of complex structures to simple relational propositions. The third algorithm, called compress, picks up where Marcu's assumption leaves off, providing a generalized fully scalable procedure for progressive reduction of relational propositions to their simplest accessible forms. These inferred reductions may then be recycled to produce RST diagrams of abridged texts. The algorithms described here are useful in positioning computational descriptions of rhetorical structures as discursive processes, allowing researchers to go beyond static diagrams and look into their formative and interpretative significance.

An Algorithmic Approach to Analyzing Rhetorical Structures

Good scientific writing makes use of specific sentence and paragraph structures, providing a rich platform for discourse analysis and developing tools to enhance text readability. In this vein, we introduce SciPara, a novel dataset consisting of 981 scientific paragraphs annotated by experts in terms of sentence discourse types and topic information. On this dataset, we explored two tasks: 1) discourse category classification, which is to predict the discourse category of a sentence by using its paragraph and surrounding paragraphs as context, and 2) discourse sentence generation, which is to generate a sentence of a certain discourse category by using various contexts as input. We found that Pre-trained Language Models (PLMs) can accurately identify Topic Sentences in SciPara, but have difficulty distinguishing Concluding, Transition, and Supporting Sentences. The quality of the sentences generated by all investigated PLMs improved with amount of context, regardless of discourse category. However, not all contexts were equally influential. Contrary to common assumptions about well-crafted scientific paragraphs, our analysis revealed that paradoxically, paragraphs with complete discourse structures were less readable.

SciPara: A New Dataset for Investigating Paragraph Discourse Structure in Scientific Papers

This paper presents evidence for an effect of genre on the use of discourse connectives in argumentation. Drawing from discourse processing research on reasoning based structures, we use fill-mask computation to measure genre-induced expectations of argument realisation, and beta regression to model the probabilities of these realisations against a set of predictors. Contrasting fill-mask probabilities for the presence or absence of a discourse connective in baseline and finetuned language models reveals that genre introduces biases for the realisation of argument structure. These outcomes suggest that cross-domain discourse processing, but also argument mining, should take into account generalisations about specific features, such as connectives, and their probability related to the genre context.

Using Discourse Connectives to Test Genre Bias in Masked Language Models

We present a pipeline for multi-lingual Shallow Discourse Parsing. The pipeline exploits Machine Translation and Word Alignment, by translating any incoming non-English input text into English, applying an English discourse parser, and projecting the found relations onto the original input text through word alignments. While the purpose of the pipeline is to provide rudimentary discourse relation annotations for low-resource languages, in order to get an idea of performance, we evaluate it on the sub-task of discourse connective identification for several languages for which gold data are available. We experiment with different setups of our modular pipeline architecture and analyze intermediate results. Our code is made available on GitHub.

Projecting Annotations for Discourse Relations: Connective Identification for Low-Resource Languages

Discourse segmentation received increased attention in the past years, however the majority of studies have focused on written genres and with high-resource languages. This paper investigates discourse segmentation of a Taiwan Southern Min spontaneous speech corpus. We compare the fine-tuning a Language Model (LLM using two approaches: supervised, thanks to a high-quality annotated dataset, and weakly-supervised, requiring only a small amount of manual labeling. The corpus used here is transcribed with both Chinese characters and romanized transcription. This allows us to compare the impact of the written form on the discourse segmentation task. Additionally, the dataset includes manual prosodic breaks labeling, allowing an exploration of the role prosody can play in contemporary discourse segmentation systems grounded in LLMs. In our study, the supervised approach outperforms weak-supervision ; character-based version demonstrated better scores compared to the romanized version; and prosodic information proved to be an interesting source to increase discourse segmentation performance.

Experimenting with Discourse Segmentation of Taiwan Southern Min Spontaneous Speech

The identification of political actors who put forward claims in public debate is a crucial step in the construction of discourse networks, which are helpful to analyze societal debates. Actor identification is, however, rather challenging: Often, the locally mentioned speaker of a claim is only a pronoun ("He proposed that [claim]"), so recovering the canonical actor name requires discourse understanding. We compare a traditional pipeline of dedicated NLP components (similar to those applied to the related task of coreference) with a LLM, which appears a good match for this generation task. Evaluating on a corpus of German actors in newspaper reports, we find surprisingly that the LLM performs worse. Further analysis reveals that the LLM is very good at identifying the right reference, but struggles to generate the correct canonical form. This points to an underlying issue in LLMs with controlling generated output. Indeed, a hybrid model combining the LLM with a classifier to normalize its output substantially outperforms both initial models.

Actor Identification in Discourse: A Challenge for LLMs?

We provide a new linguistic resource: The Minecraft Structured Dialogue Corpus (MSDC), a discourse annotated version of the Minecraft Dialogue Corpus (MDC), with complete discourse structures for the situated dialogues in the style of SDRT. Our structures feature both linguistic discourse moves and nonlinguistic actions.

Discourse Structure Annotations for the Minecraft Dialogue Corpus

Stress detection from social media texts has proved to play an important role in mental health assessments. People tend to express their stress on social media more easily. Analysing and classifying these texts allows for improvements in development of recommender systems and automated mental health assessments. In this paper, a GPT model is used for classification of social media texts into two classes - stressed and not-stressed. The texts used for classification are in two Dravidian languages - Tamil and Telugu. The results, although not very good shows a promising direction of research to use GPT models for classification.

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Zero-shot learning for multilingual discourse relation classification

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CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT

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Zero-shot learning for multilingual discourse relation classification

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CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT

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