Papers by Oana Balalau

9 papers
Discovering the Functions of Language in Online Forums (D19-55)

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Challenge: a vast amount of work has been dedicated to speech act categorization for characterizing discourses . lack of formalism and diversity of taxonomies make it difficult to compare different annotated datasets.
Approach: They propose a semi-supervised framework for predicting the functions of Reddit comments . they propose to use the framework to analyze online forum conversations .
Outcome: The proposed framework can predict functions of Reddit comments and 165K comments.
Structured Discourse Representation for Factual Consistency Verification (2025.findings-acl)

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Challenge: Existing methods to verify factual consistency of text capture a performance gap compared with sentence-level entailment.
Approach: They propose a method that combines structured discourse information extraction with a classifier, FDSpotter, for factual consistency verification.
Outcome: The proposed method achieves competitive performance on two tasks: data-to-text generation and text summarisation.
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation (2023.findings-emnlp)

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Challenge: Graph-to-text (G2T) generation is an important task in natural language generation as it renders graphs accessible to non-technical users in downstream applications such as question answering.
Approach: They propose a metric that correctly identifies factual faithfulness and uses it to determine if a triple is present in a generated text.
Outcome: The proposed metric achieves highest correlation with human annotations on data correctness, data coverage, and relevance.
The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications (2025.findings-emnlp)

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Challenge: public repositories of relations of interest are increasingly recommended or mandated in various domains to avoid COIs.
Approach: They propose an LLM-based open information extraction framework for extracting financial or other types of interesting relations from scientific text.
Outcome: The proposed framework extracts financial or other types of interesting relations from scientific text.
From the Stage to the Audience: Propaganda on Reddit (2021.eacl-main)

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Challenge: a recent opinion piece in the Washington Post highlights a difference between the political discourse in the two countries.
Approach: They analyze political forums on Reddit that target a diverse audience in two countries . they focus on three research questions: who is posting propaganda? and how is propaganda received?
Outcome: The authors analyze political forums on reddit in the US and the UK for one year . they find that propaganda is misleading and is received by different audiences .
Navigating the Political Compass: Evaluating Multilingual LLMs across Languages and Nationalities (2025.findings-acl)

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Challenge: Large Language Models (LLMs) are ubiquitous in today’s technological landscape, boasting a plethora of applications, and even endangering human jobs in complex and creative fields.
Approach: They evaluate the political bias of 15 multilingual LLMs using the Political Compass Test and assign a nationality to each model.
Outcome: The models on the 50 most populous countries and their official languages exhibit political bias.
Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions (2021.acl-long)

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Challenge: a number of people engage in unsound argumentation techniques to prove a claim on online platforms . fallacies are weak arguments that seem convincing, but their evidence does not prove or disprove the conclusion .
Approach: They propose to use user comments containing fallacy mentions as noisy labels to classify fallacies . they use the pragma-dialectical theory of argumentation to study the most common fallacias on Reddit .
Outcome: The proposed dataset of fallacies on reddit shows that neural models perform better in conversational context.
Open Information Extraction with Entity Focused Constraints (2023.findings-eacl)

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Challenge: Open Information Extraction (OIE) is the task of extracting tuples from unstructured corpora without any knowledge of the type and lexical form of the subject, the object, or the subject.
Approach: They exploit domain knowledge to inject constraints into the extraction through constrained inference and constraint-aware training.
Outcome: The proposed approach improves the CaRB and WIRe57 metric and achieves a 29.17% improvement in the CARB and 24.37% improvement on the WIRe56 metric.
Benchmarking the Benchmarks: Reproducing Climate-Related NLP Tasks (2025.findings-acl)

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Challenge: a recent study examines the use of climate-related natural language processing (NLP) for climate-relevant tasks.
Approach: They perform a reproducibility study on 8 tasks and 29 datasets, testing 6 models.
Outcome: The proposed models are based on 8 tasks and 29 datasets.

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