China

This paper investigates stance detection on Nigerian 2023 election tweets by comparing transformer-based and classical machine learning models. A balanced dataset of 2,100 annotated tweets was constructed, and BERT-base-uncased was fine-tuned to classify stances into Favor, Neutral, and Against. The model achieved 98.1% accuracy on an 80/20 split and an F1-score of 96.9% under 5-fold cross-validation. Baseline models such as Naïve Bayes, Logistic Regression, Random Forest, and SVM were also evaluated, with SVM achieving 97.6% F1. While classical methods remain competitive on curated datasets, BERT proved more robust in handling noisy, sarcastic, and ambiguous text, making it better suited for real-world applications in low-resource African NLP contexts.

EMNLP 2025

Stance Detection on Nigerian 2023 Election Tweets Using BERT: A Low-Resource Transformer-Based Approach

cpu-based training

political discourse analysis

nigerian 2023 presidential election

bert-base-uncased

twitter data

low-resource nlp

transformer models

machine learning

stance detection

data augmentation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

workshop paper

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community."<br>

*Message from the General Chair, Dirk Hovy*





<br>

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

Code-switching (CSW) in speech is motivated by conversational factors across levels of linguistic analysis. While we know much about why speakers code-switch, there remains great scope for exploring how CSW occurs in speech, particularly within the discourse-level linguistic context. We build on prior work by asking: how are patterns of CSW influenced by different conversational contexts spanning Academic, Cultural, Personal, and Professional discourse topics? To answer this, we annotate a Mandarin-English spontaneous speech corpus, and analyze its discourse topics alongside various aspects of CSW production. We show that discourse topics interact significantly with utterance-level CSW, resulting in distinctive patterns of CSW presence, richness, language direction, and syntax that are uniquely associated with different contexts. Our work is the first to take such a context-sensitive approach to studying CSW, contributing to a broader understanding of the discourse topics that motivate speakers to code-switch in diverse ways.

Code-switching in Context: Investigating the Role of Discourse Topic in Bilingual Speech Production

Discourse adverbials are key features of discourse coherence, but their function is often ambiguous. In this work, we investigate how the discourse function of otherwise varies in different contexts. We revise the function set in Rohde et al. (2018b) to account for a new meaning we have encountered. In turn, we create the "otherwise" corpus, a dataset of naturally occurring passages annotated for discourse functions, and identify lexical signals that make a function available with a corpus study. We define continuation acceptability, a metric based on surprisal to probe language models for what they take the function of otherwise to be in a given context. Our experiments show that one can improve function inference by focusing solely on tokens up to and including the head verb of the continuation (i.e., otherwise clause) that have the most varied surprisal across function-disambiguating discourse markers. Lastly, we observe that some of these tokens confirm lexical signals we found in our earlier corpus study, which provides some promising evidence to motivate future pragmatic studies in language models

"Otherwise" in Context: Exploring Discourse Functions with Language Models

Understanding and interpreting culturally specific language remains a significant challenge for multilingual natural language processing (NLP) systems, particularly for less-resourced languages. To address this problem, this paper introduces PRONE, a novel dataset of 2,830 Nepali proverbs, and evaluates the performance of various language models (LMs) in two tasks: (i) identifying the correct meaning of a proverb from multiple choices, and (ii) categorizing proverbs into predefined thematic categories. The models, including both open-source and proprietary, were tested in zero-shot and few-shot settings with prompts in English and Nepali. While models like GPT-4o demonstrated promising results and achieved the highest performance among LMs, they still fall short of human-level accuracy in understanding and categorizing culturally nuanced content, highlighting the need for more inclusive NLP.

Probing the Limits of Multilingual Language Understanding: Low-Resource Language Proverbs as LLM Benchmark for AI Wisdom

Large Language Models (LLMs) have demonstrated remarkable performance across various NLP tasks, yet they continue to face challenges in discourse relation recognition (DRR). Current state-of-the-art methods for DRR primarily rely on smaller pre-trained language models (PLMs). In this study, we conduct a comprehensive analysis of different approaches using both PLMs and LLMs, evaluating their effectiveness for DRR at multiple granularities and under different data availability settings. Our findings indicate that no single approach consistently outperforms the others, and we offer a general comparison framework to guide the selection of the most appropriate model based on specific DRR requirements and data conditions.

Discourse Relation Recognition with Language Models Under Different Data Availability

Consider the example "The bird sang the nursery rhyme beautifully. It made everyone in the room smile". The pronoun 'it' here refers either to the bird or to the event of singing. This example is inherently ambiguous. It cannot be meaningfully disambiguated as an event or entity reference, as both readings result in the same text meaning. This study introduces a new dataset EMBITEXT to preserve ambiguity in the language by navigating through the ambiguity surrounding the pronominal reference to the entity or event. Oftentimes, ambiguity does not necessarily need to be resolved but is modelled carefully. Furthermore, this study explores the capacity of LLMs (Llama, Mistral, Gemini, Claude AI) to embrace ambiguity in generating text that exhibit referential ambiguity via an In-Context learning approach. To evaluate of the dataset, RoBERTa was finetuned on this data to model ambiguity while simultaneously distinguishing between entity or event references. Results demonstrate EmbiText's capacity to advance the ongoing NLP research by modelling linguistic ambiguity in computational environments instead of fully disambiguating it, thereby retaining diverse interpretations where resolution may alter meaning.

EmbiText: Embracing Ambiguity by Annotation, Recognition and Generation of Pronominal Reference with Event-Entity Ambiguity

Understanding the strategies that make expert-led explanations effective is a core challenge in didactics and a key goal for explainable AI. To study this computationally, we introduce ReWIRED, a large corpus of explanatory dialogues annotated by education experts with fine-grained, span-level teaching acts across five levels of explainee knowledge. We use this resource to assess the capabilities of modern language models, finding that while few-shot LLMs struggle to label these acts, fine-tuning is a highly effective methodology. Moving beyond structural annotation, we propose and validate a suite of didactic quality metrics. We demonstrate that a prompt-based evaluation using an LLM as a ``judge'' is required to capture how the functional quality of an explanation aligns with the learner's expertise -- a nuance missed by simpler static metrics. Together, our dataset, modeling insights, and evaluation framework provide a comprehensive methodology to bridge pedagogical principles with computational discourse analysis.

Human and LLM-based Assessment of Teaching Acts in Expert-led Explanatory Dialogues

We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark mono-treebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono‑treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.

Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser

We present a dataset of text revisions involving the deletion or replacement of discourse connectives. Manual annotation of a replacement subset reveals that only 19% of edits were judged either necessary or should be left unchanged, with the rest appearing optional. Surprisal metrics from GPT-2 token probabilities and prompt-based predictions from GPT-4.1 correlate with these judgments, particularly in such clear cases.

Information-Theoretic and Prompt-Based Evaluation of Discourse Connective Edits in Instructional Text Revisions

Previous work has shown that simple mask-filling can provide useful information about the discourse informativeness of syntactic structures. Dong et al. (2024) first adopted this approach to investigating preposing constructions. The problem with single token mask fillers was that they were, by and large, ambiguous. We address the issue by adapting the approach of Kalinsky et al. (2023) to support the prediction of multi-token connectives in masked positions. Our first experiment demonstrates that this multi-token mask-filling approach substantially outperforms the previously considered single-token approach in recognizing implicit discourse relations. Our second experiment corroborates previous findings, providing additional empirical support for the role of preposed syntactic constituents in signaling discourse coherence. Overall, our study extends existing mask-filling methods to a new discourse-level task and reinforces the linguistic hypothesis concerning the discourse informativeness of preposed structures.

Multi-token Mask-filling and Implicit Discourse Relations

This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2025 shared task on discourse: segmentation, connective identification, and relation classification. 
The competition involves corpora in various languages, in several underlying frameworks, and datasets are given with or without sentence segmentation. 
This year, for the ranked, closed track, the campaign adds as a constraint to train only one model for each task, with an upper bound on the size of the model (no more than 4B parameters).
An additional open track authorizes any size of, possibly non public, models that will not be reproduced by the organizers and thus not ranked.
We compared several fine-tuning approaches either based on encoder-only transformer-based models, or auto-regressive generative ones. 
To be able to train one model on the variety of corpora, we explored various ways of combining data -- by framework, language or language groups, with different sequential orderings --, and the addition of features to guide the model. 
For the closed track, our final submitted system is based on XLM-RoBERTa large for relation identification, and on InfoXLM for segmentation and connective identification. 
Our experiments demonstrate that building a single, multilingual model does not necessarily degrade the performance compared to language-specific systems, with at best 64.06% for relation identification, 90.19% for segmentation and 81.15% for connective identification (on average on the development sets), results that are similar or higher that the ones obtained in previous campaigns.
We also found that a generative approach could give even higher results on relation identification, with at best 64.65% on the dev sets.

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