Austria

Sign language segmentation focuses on identifying temporal boundaries within sign language videos. As compared to previous segmentation techniques that have depended on frame-level and phrase-level segmentation, our study emphasizes on subtitle-level segmentation, using synchronized subtitle data to facilitate temporal boundary recognition. Based on Beginning-Inside-Outside (BIO) tagging for subtitle unit delineation, we train a sequence-to-sequence (Seq2Seq) model with and without attention for subtitle boundary identification. Training on optical flow data and aligned subtitles from BOBSL and YouTube-ASL, we show that the Seq2Seq model with attention outperforms baseline models, achieving improved percentage of segments, F1 and IoU score. An additional contribution is the development of an method for subtitle temporal resolution, aiming to facilitate manual annotation.

ACL 2025

Sign Language Video Segmentation Using Temporal Boundary Identification

subtitle temporal resolution

optical flow

sign language

segmentation

sequence models

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

poster

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Large Language Models (LLMs) can enhance the performance of Named Entity Recognition (NER) tasks by leveraging external knowledge through in-context learning. When it comes to entity-type-related external knowledge, existing methods mainly provide LLMs with semantic information such as the definition and annotation guidelines of an entity type, leaving the effect of orthographic or morphological information on LLM-based NER unexplored. Besides, it is non-trivial to obtain literal patterns written in natural language to serve LLMs. In this work, we propose LiP-NER, an LLM-based NER framework that utilizes \textbf{Li}teral \textbf{P}atterns, the entity-type-related knowledge that directly describes the orthographic and morphological features of entities. We also propose an LLM-based method to automatically acquire literal patterns, which requires only several sample entities rather than any annotation example, thus further reducing human labor. Our extensive experiments suggest that literal patterns can enhance the performance of LLMs in NER tasks. In further analysis, we found that entity types with relatively standardized naming conventions but limited world knowledge in LLMs, as well as entity types with broad and ambiguous names or definitions yet low internal variation among entities, benefit most from our approach. We found that the most effective written literal patterns are (1) detailed in classification, (2) focused on majority cases rather than minorities, and (3) explicit about obvious literal features.

LIP-NER: Literal Patterns Benefit LLM-Based NER

It is widely assumed that Large Language Models (LLMs) are shaping language, with multiple studies noting the growing presence of LLM-generated content and suggesting homogenizing effects. However, it remains unclear if these effects are already evident in recent writing. This study addresses that gap by comparing two datasets of English online news articles -- one from 2018, prior to LLM popularization, and one from 2024, after widespread LLM adoption. We define lexical homogenization as a decrease in lexical diversity, measured by the MATTR, Maas, and MTLD metrics, and introduce the LLM-Style-Word Ratio (SWR) to measure LLM influence. We found higher MTLD and SWR scores, yet negligible changes in Maas and MATTR scores in 2024 corpus. We conclude that while there is an apparent influence of LLMs on written online English, homogenization effects do not show in the measurements. We therefore propose to apply different metrics to measure lexical homogenization in future studies on the influence of LLM usage on language change.

Testing English News Articles for Lexical Homogenization Due to Widespread Use of Large Language Models

Producing trustworthy and reliable Large Language Models (LLMs) has become increasingly important as their usage becomes more widespread. Calibration seeks to achieve this by improving the alignment between the model's confidence and the actual likelihood of its responses being correct or desirable. However, it has been observed that the internal confidence of a model, derived from token probabilities, is not well aligned with its verbalized confidence, leading to misleading results with different calibration methods. In this paper, we propose Direct Confidence Alignment (DCA), a method using Direct Preference Optimization to align an LLM’s verbalized confidence with its internal confidence rather than ground-truth accuracy, enhancing model transparency and reliability by ensuring closer alignment between the two confidence measures. We evaluate DCA across multiple open-weight LLMs on a wide range of datasets. To further assess this alignment, we also introduce three new calibration error-based metrics. Our results show that DCA improves alignment metrics on certain model architectures, reducing inconsistencies in a model's confidence expression. However, we also show that it can be ineffective on others, highlighting the need for more model-aware approaches in the pursuit of more interpretable and trustworthy LLMs.

Direct Confidence Alignment: Aligning Verbalized Confidence with Internal Confidence In Large Language Models

Static and outdated datasets hinder the accuracy of Financial Sentiment Analysis (FSA) in capturing rapidly evolving market sentiment. We tackle this by proposing a novel data augmentation technique using Retrieval Augmented Generation (RAG). Our method leverages a generative LLM to infuse established benchmarks with up-to-date contextual information from contemporary financial news. While this RAG-based augmentation significantly modernizes the data's alignment with current financial language, we employ a robust BERT-BiGRU judge model to ensure the sentiment of the original annotations is faithfully preserved. Crucially, FSA models trained on this enriched data exhibit enhanced performance on unseen test sets, demonstrating the practical value of our approach for developing more reliable and current sentiment classifiers.

Bridging the Data Gap in Financial Sentiment: LLM-Driven Augmentation

Voice conversion has emerged as a pivotal technology in numerous applications ranging from assistive communication to entertainment. In this paper, we present RT-VC, a zero-shot real-time voice conversion system that delivers ultra-low latency and high-quality performance. Our approach leverages an articulatory feature space to naturally disentangle content and speaker characteristics, facilitating more robust and interpretable voice transformations. Additionally, the integration of differentiable digital signal processing (DDSP) enables efficient vocoding directly from articulatory features, significantly reducing conversion latency. Experimental evaluations demonstrate that, while maintaining synthesis quality comparable to the current state-of-the-art (SOTA) method, RT-VC achieves a CPU latency of 61.4 ms, representing a 13.3% reduction in latency. Project page: https://berkeley-speech-group.github.io/RT-VC/

RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding

Machine translation (MT) systems that support low-resource languages often struggle on specialized domains. While researchers have proposed various techniques for domain adaptation, these approaches typically require model fine-tuning, making them impractical for non-technical users and small organizations. To address this gap, we propose Tulun, a versatile solution for terminology-aware translation, combining neural MT with large language model (LLM)-based post-editing guided by existing glossaries and translation memories.
Our open-source web-based platform enables users to easily create, edit, and leverage terminology resources, fostering a collaborative human-machine translation process that respects and incorporates domain expertise while increasing MT accuracy.
Evaluations show effectiveness in both real-world and benchmark scenarios: on medical and disaster relief translation tasks for Tetun and Bislama, our system achieves improvements of 16.90-22.41 ChrF++ points over baseline MT systems. Across six low-resource languages on the FLORES dataset, Tulun outperforms both standalone MT and LLM approaches, achieving an average improvement of 2.8 ChrF points over NLLB-54B. Tulun is publicly accessible at https://bislama-trans.rapha.dev.

Tulun: Transparent and Adaptable Low-resource Machine Translation

DEEP is a bidirectional translation system for the Italian Sign Language, tailored to two specific, common use cases: pharmacy and the registry office of the municipality, for which a custom corpus has been collected. Two independent pipelines permit to create a chat-like interaction style, where the deaf subject just signs in front of a camera, and see a virtual LIS interpreter, while the hearing subject read and write messages into a chat UI. The LIS-to-Italian pipeline leverages, in a novel way, a customized Whisper model (a well-known speech recognition system), by means of "pseudo-spectrograms''. The Italian-to-LIS pipeline leverages a virtual avatar created with Viggle.ai. DEEP has been evaluated with LIS signers, obtaining very promising results.

DEEP: an automatic bidirectional translator leveraging an ASR for translation of Italian sign language

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from generating inaccurate content or fabricating information without valid sources. To address these issues, we propose LCDS, a tool for empowering LLMs with Logic-Controlled Discharge Summary generation. LCDS constructs a source mapping table by calculating the textual similarity between electronic medical records (EMRs) and discharge summaries, providing a structured reference for generation. Based on a comprehensive set of logical rules, LCDS identifies the structured writing logic of discharge summaries and integrates it with EMRs to generate silver discharge summaries. Furthermore, LCDS traces the provenance of generated content, allowing experts to review, provide feedback, and rectify errors to produce golden discharge summaries, which are subsequently recorded for the incremental fine-tuning of LLMs.
Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.

LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review

The study of generalization in Language Models (LMs) requires controlled experiments that can precisely measure complex linguistic variations between training and testing datasets. We introduce DECAF, a framework that enables the analysis and filtering of linguistically-annotated datasets down to the character level. Rather than creating new resources for each experiment, DECAF starts from datasets with existing linguistic annotations, and leverages them to analyze, filter, and generate highly controlled and reproducible experimental settings targeting specific research questions. We demonstrate DECAF's functionality by adding full morphology and dependency annotations to the 131M-token BabyLM corpus and indexing the resulting 1.1B annotations to analyze its internal domain variance, and to create a controlled training data curriculum for a small-scale gender bias study. We release DECAF as a fully open-source Python library, along with the parsed and indexed version of BabyLM, as resources for future generalization research.

DECAF: A Dynamically Extensible Corpus Analysis Framework

We introduce FORG3D, a 3D rendering toolkit developed with Blender and Python, which synthesizes vision-language data for two primary purposes: (1) supporting human cognitive experiments that require fine-grained control over material and (2) analyzing and improving the visual reasoning capabilities of large vision-language models. The toolkit provides flexible and precise control over object placement, orientation, inter-object distances, and camera configurations while automatically generating detailed spatial metadata. Additionally, it includes a built-in feature for integrating AI-generated backgrounds, enhancing the realism of synthetic scenes. FORG3D is publicly available at https://github.com/compling-wat/FORG3D, and a video demonstration is available at https://www.youtube.com/watch?v=QvIqib_PU8A.

Downloads

Next from ACL 2025

LIP-NER: Literal Patterns Benefit LLM-Based NER

Similar lecture

Unsupervised Subtitle Segmentation with Masked Language Models