United States

Quantitative research in international relations relies heavily on structured event data, yet existing automated datasets lack up-to-date coverage of both conflictual and cooperative interactions. We introduce GENOME (Geopolitical Event News Observatory, Mapping, and Extraction), an automatically extracted dataset that implements PLOVER’s 16 event types and extends its Actor–Recipient schema with a Third Party role to capture multilateral relations from newswire data. GENOME’s pipeline comprises event extraction, ontology-based classification, entity normalization, and deduplication, leveraging GPT models with one-shot prompting and enforced structured outputs. We compare GENOME against POLECAT dataset over a five-month overlap period across event volume, temporal dynamics, and geographical coverage. Results show that while the two datasets align closely on conflict event types, GENOME captures a more balanced distribution of cooperative events, particularly verbal interactions nearly absent in POLECAT. GENOME also demonstrates improved temporal precision by attributing events to their inferred date of occurrence rather than publication date, and effective deduplication of highly covered events.

ACL 2026

GENOME: A New Geopolitical Event Methodology and Dataset using Large Language Models

plover

geopolitical events

event data

international relations

large language models

event extraction

natural language processing

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. The results show that, while most models perform well in binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and demonstrate the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.

POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization

Simultaneous speech-to-speech translation (SimulS2ST) enables real-time cross-lingual communication, but existing evaluation has focused largely on short or pre-segmented speech rather than long-form, continuous input. Prior approaches are difficult to reproduce and make assumptions that do not hold for end-to-end systems. We present a practical evaluation method for long-form SimulS2ST. Given source speech, pre-segmented source transcripts, and reference translations, we run automatic speech recognition (ASR) and forced alignment on the generated target speech to recover token-level timestamps, then apply a sentence-embedding-based aligner to match the target text to its corresponding source sentences. This enables sentence-level computation of latency and quality metrics, including YAAL and xCOMET, which are then aggregated into final system-level scores. Experiments on representative SimulS2ST systems show that the method is effective in practice and reveal that current systems suffer from substantial latency accumulation on long speech.

A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation

RAG systems retrieve documents optimized for answering *one query at a time*. Yet enterprise users arrive with *sessions*, that is, coherent episodes of related questions that span semantically distant parts of the knowledge base. We show that a single retrieval call over a standard knowledge base covers only 41% of a user's session-level information need. To close this gap, we reorganize the KB offline using co-occurrence-aware clustering and expand retrieval candidates through cluster neighborhoods at query time. On WixQA (6,221 enterprise support articles), our method raises single-query session coverage to 58% (+17% absolute; 95% CI: [14.1, 20.4]), reduces retrieval calls to 70% coverage by 34%, and compresses the KB to 20% of its original size, all consistently across four embedding models and six functional domains. We argue that session-level coverage, not single-query recall, should be the primary metric for enterprise RAG evaluation.

One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG

Object recognition models are well studied on benchmark datasets, typically focusing on performance in retrieving objects that exist in images. However, in real-life scenarios there is no prior knowledge of an object's existence, and current research fails to assess model performance in these situations. This research aims to shed light on this problem by testing three Open-World models, YOLO-World, Grounding Dino and GPT-4o, on the LVIS, Open Images, and JUS datasets. We design an experiment where models are confronted with impossible prompts by instructing them to retrieve non-existing objects. This allows us to observe the models' uncertainty performance. Overall, GPT-4o performed poorest with regard to object recognition and uncertainty estimation. GPT-4o showed to be highly overconfident. In contrast, YOLO-World and Grounding Dino are slightly underconfident, but they are superior in their uncertainty calibration in comparison to GPT-4o. However, all three models occasionally assign high confident predictions to non-existing objects. Showing that improvement can still be made to the uncertainty estimation of these models when confronted with impossible prompts.

Tricking Open-World Object Recognition Models: Uncertainty in Out-of-Distribution Detection

In factual question answering, many errors are not failures of access but failures of commitment: the system retrieves relevant evidence, yet still settles on the wrong answer. We present CounterRefine, a lightweight inference-time repair layer for retrieval-grounded question answering. CounterRefine first produces a short answer from retrieved evidence, then gathers additional support and conflicting evidence with follow-up queries conditioned on that draft answer, and finally applies a restricted refinement step that outputs either KEEP or REVISE, with proposed revisions accepted only if they pass deterministic validation. In effect, CounterRefine turns retrieval into a mechanism for testing a provisional answer rather than merely collecting more context. On the full SimpleQA benchmark, CounterRefine improves a matched GPT-5 Baseline-RAG by 5.8 points and reaches a 73.1 percent correct rate, while exceeding the reported one-shot GPT-5.4 score by roughly 40 points. These findings suggest a simple but important direction for knowledgeable foundation models: beyond accessing evidence, they should also be able to use that evidence to reconsider and, when necessary, repair their own answers.

CounterRefine: Answer-Conditioned Counterevidence Retrieval for Inference-Time Knowledge Repair in Factual Question Answering

Large language models (LLMs) are commonly described as acquiring knowledge through large scale pretraining on textual corpora. This view underestimates the epistemic consequences of post training safety mechanisms. Modern LLMs undergo extensive safety alignment via curated datasets, human annotations, and reinforcement learning from human feedback (RLHF), processes that do not merely constrain outputs but actively restructure how propositional and procedural knowledge is represented, accessed, and expressed. We argue that safety alignment should be understood as a systematic form of knowledge editing at scale. Annotation frameworks used to construct safety datasets function as normative ontologies that partition language into categories of acceptable and unacceptable content, and alignment training embeds these categorical distinctions into model parameters. We introduce the Safety Knowledge Pipeline (SKP), a four stage conceptual framework describing how pretraining knowledge is progressively filtered, reframed, and constrained through annotation and alignment mechanisms. We identify three mechanisms of knowledge modification: suppression, reframing, and substitution, each with distinct diagnostic signals, and we operationalize them in a cross lingual evaluation protocol. Case studies spanning harmful instruction queries, hate speech annotation in Arabic dialects, and culturally variable discourse illustrate the framework. We further discuss how treating annotator disagreement as a training signal rather than noise can mitigate the culturally hegemonic effects of current alignment pipelines. Our analysis has direct implications for LLM factuality, cross cultural equity, and the governance of AI safety systems.

Annotation Frameworks Shape Model Knowledge: Safety Alignment in Large Language Models

We present an annotation schema for Haitian Creole medical translation that makes clinical risk and sociolinguistic variation explicit while remaining lightweight enough for small expert teams. The schema includes binary fields for overall acceptability, severity of potential misunderstanding, and foreign-influence cues, along with conditional error tags aligned with Multidimensional Quality Metrics (MQM), commonly used in the medical domain, for interoperability. Through three rounds of annotation and adjudication we achieve stable inter-annotator agreement and release a gold dataset of 152 EN$\rightarrow$HC medical sentence pairs. A simple classifier–labeller baseline demonstrates that acceptability and severity are reliably learnable under data scarcity, while foreign-influence judgments remain limited by prevalence. These results show that clinically oriented, variety-sensitive annotation can both support immediate screening of patient-facing translations and provide reward-ready signals for future preference-based MT and LLM fine-tuning.

Annotating Clinical Risk and Variation in Haitian Creole Medical Translation

We propose a simplified human-in-the-loop workflow for second language (L2) Korean morphosyntactic annotation by leveraging agreement between two domain-adapted parsers. We first evaluate whether parser agreement can serve as a proxy for annotation correctness by comparing it with independent human judgments. The results show strong correspondence between parser and human judgments, supporting the feasibility of semi-automatic L2-Korean UD annotation. Further analysis demonstrates that parser disagreements cluster in linguistically predictable domains such as grammatical-relation distinctions and clause-boundary ambiguity. While many disagreement cases are tractable for iterative model refinement, others reflect deeper representational challenges inherent in parsing and tagging L2-Korean corpora.

Parser agreement and disagreement in L2 Korean UD: Implications for human-in-the-loop annotation

In this paper, we present a set of linguistic rules, employed to enhance the readability of legal texts. The rules were compiled and implemented as a rule-based module of PONK, an advisory tool that contributes to simplification and higher clarity of Czech legal texts, especially those destined to non-expert audience. Based on recurring phenomena in authentic texts and relevant scientific sources, the rules cover mainly the domains of syntax and lexicon. In addition, we present the results of application of the rules to a corpus of authentic legal texts, evaluated by a human annotator, and examine their impact.

Rules-based system for Czech legal text readability

Large annotated datasets inevitably contain errors that are costly to identify via manual review. We study a human-AI annotation error auditing workflow using frontier Large Language Models (LLMs), focusing on Hebrew diacritization. We take the the EACL 2023 Hebrew Homograph Challenge Set as our test case. In a focused evaluation on 12 of the homograph sets with 271 confirmed errors (verified through exhaustive manual review of all 7,241 sentences), Gemini 3 Pro achieves 83.6% recall (95% Confidence Interval: [79.3%, 88.2%]) and 99.1% precision - substantially higher than other frontier LLMs. Furthermore, two independent human experts achieved 62.4% and 42.8% recall respectively, a 20-percentage-point spread that reflects the difficulty of sparse-target error search. Even the union of both experts' findings (73.4% recall) falls short of a single LLM run (83.6%), while LLM-aided auditing reduces review effort by over 95%. We analyze the trade-offs between batch size and recall, and release both a human-verified Gold Standard with per-error difficulty annotations and a globally corrected version of the Challenge Set.

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