FRAMENET

Verb Alternations and Their Impact on Frame Induction (N18-4)

Challenge:	This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics .
Approach:	This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics .
Outcome:	This workshop will present current research on aligning Frame Semantic resources across languages and automatic frame semantic parsing in English and other languages.

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Esther Seyffarth

Challenge:	Frame induction is the automatic creation of frame-semantic resources similar to FrameNet or PropBank, which map lexical units of a language to frame representations of each lexical unit’s semantics.
Approach:	They propose to use frames to map lexical units to frame representations of each lexical unit's semantics.
Outcome:	The proposed framework compares the semantics of alternating verbs and their similarities and differences.

Unsupervised Semantic Frame Induction using Triclustering (P18-2)

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Dmitry Ustalov, Alexander Panchenko, Andrey Kutuzov, Chris Biemann, Simone Paolo Ponzetto

Semi-automatic Korean FrameNet Annotation over KAIST Treebank (L18-1)

Challenge:	Recent work on frame-semantics has enabled the development of wide-coverage frame parsers using supervised learning.
Approach:	They propose to use dependency triples to perform unsupervised frame induction on a Web-scale corpus.
Outcome:	The proposed approach performs state-of-the-art on a FrameNet-derived dataset and performs on par with competitive methods on . verb class clustering task.

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Younggyun Hahm, Jiseong Kim, Sunggoo Kwon, Key-Sun Choi

Syntactic Text Processing

Challenge:	Annotating FrameNet over raw sentences is an expensive and complex task, because of which we have designed a semi-automatic annotation approach.
Approach:	They propose to use Korean FrameNet annotations to build a frame-semantic parser for English using full-text annotation and partially annotated exemplar sentences to train their models.
Outcome:	The proposed model is based on a lexical database of the Korean FrameNet, and its current scope, status, and limitations are discussed in the paper.

LOME: Large Ontology Multilingual Extraction (2021.eacl-demos)

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Patrick Xia, Guanghui Qin, Siddharth Vashishtha, Yunmo Chen, Tongfei Chen, Chandler May, Craig Harman, Kyle Rawlins, Aaron Steven White, Benjamin Van Durme

Robust Semantic Parsing with Adversarial Learning for Domain Generalization (N19-2)

Challenge:	LOME is a system for performing multilingual information extraction with large ontologies.
Approach:	They propose a system for multilingual information extraction with a framenet parser . LOME is available as a Docker container on Docker Hub and a lightweight version is available on the web .
Outcome:	The proposed system outperforms or is competitive with the (monolingual) state-of-the-art . it can be used to build knowledge graphs with large ontologies and across multiple languages .

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Gabriel Marzinotto, Géraldine Damnati, Frédéric Béchet, Benoît Favre

Information Retrieval

Challenge:	Using adversarial learning to train models on a higher level of abstraction to increase their robustness to lexical and stylistic variations is crucial for the integration of Semantic Parsing technologies in real applications.
Approach:	They propose to perform Semantic Parsing with a domain classification adversarial task and an unsupervised domain discovery approach that yields equivalent improvements.
Outcome:	The proposed approach improves on a French corpus of encyclopedic documents annotated with FrameNet and an unsupervised domain discovery approach yields equivalent improvements.

Mining Logical Event Schemas From Pre-Trained Language Models (2022.acl-srw)

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Lane Lawley, Lenhart Schubert

Language Models

Challenge:	a pre-trained language model is induced into acting as a distribution over stories, a new system is proposed . NESL is a neural event schema learning system that combines large language models, FrameNet parsing, and simple behavioral schemas to bootstrap the learning process.
Approach:	They propose a neural event schema learning system that bootstraps the learning process by parsing pre-trained language models into simple behavioral schemas.
Outcome:	The proposed system combines large language models, a powerful logical representation of language, and simple behavioral schemas to bootstrap the learning process.

FrameForm: An Open-source Annotation Interface for FrameNet (2021.eacl-demos)

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Büşra Marşan, Olcay Taner Yıldız

Natural Language Interfaces

Challenge:	FrameNet is a computational lexicography tool that provides in-depth semantic information regarding the argument structure and thematic relations of a predicate.
Approach:	They introduce an open-source annotation tool that can be easily modified to accommodate predicate annotations based on Frame Semantics.
Outcome:	The proposed tool can be easily modified to answer the annotation needs of a wide range of languages.

Crowdsourcing in the Development of a Multilingual FrameNet: A Case Study of Korean FrameNet (2020.lrec-1)

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Younggyun Hahm, Youngbin Noh, Ji Yoon Han, Tae Hwan Oh, Hyonsu Choe, Hansaem Kim, Key-Sun Choi

Iterative Paraphrastic Augmentation with Discriminative Span Alignment (2021.tacl-1)

Challenge:	Using current methods, the construction of multilingual FrameNets is expensive and complex.
Approach:	They evaluated whether crowdsourcing approaches captured cross-cultural and cross-linguistic meanings . they found that crowd workers made intuitive choices comparable to trained FrameNet experts .
Outcome:	The results are now available in Korean FrameNet 1.1.

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Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme

Paraphrasing

Challenge:	Existing datasets can be expanded or created using a small, manually produced seed corpus.
Approach:	They propose a paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrases and discriminative span alignment.
Outcome:	The proposed approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus.

R2A-TLS: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Integration (2025.findings-emnlp)

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Chenlong Bao, Shijie Li, Minghao Hu, Ming Qiao, Bin Zhang, Jin-Tao Tang, Shasha Li, Ting Wang

Information Retrieval

Challenge:	Existing methods struggle to capture coherent event narratives due to fragmented descriptions . Existing approaches accumulate noise through iterative retrieval strategies that lack relevance evaluation.
Approach:	They propose a reflective retrieval-augmented timeline summarization with Causal-Semantic Intergration approach for open-domain timeline summarizing .
Outcome:	The proposed approach outperforms the best prior published approaches.

A FrameNet for Cancer Information in Clinical Narratives: Schema and Annotation (L18-1)

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Kirk Roberts, Yuqi Si, Anshul Gandhi, Elmer Bernstam

Challenge:	Existing natural language processing (NLP) systems for cancer-related information are highly task-specific and often produce incompatible annotations and algorithms.
Approach:	They propose a general-purpose natural language processing resource for cancer-related information in clinical notes . the project uses a frame semantic method to emphasize the information presented in the notes themselves .
Outcome:	The proposed project emphasizes the information presented in the clinical notes and its linguistic structure.

Adverbs, Surprisingly (2023.starsem-1)

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Dmitry Nikolaev, Collin Baker, Miriam R. L. Petruck, Sebastian Padó

Challenge:	adverbs are the part of speech (POS) that has seen the least attention in computational linguistics due to its challenging nature.
Approach:	They propose to use Frame Semantics to characterize word meaning to uncover systematic gaps in adverb accounts.
Outcome:	The proposed approach can describe ambiguity, semantic roles, and null instantiation of adverbs.

Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs (2026.acl-short)

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Shahid Iqbal Rai, Danilo Croce, Roberto Basili

Annotating FrameNet via Structure-Conditioned Language Generation (2024.acl-short)

Challenge:	Large language models (LLMs) are fluent but often brittle when interpretation depends on external information.
Approach:	They propose a framework that injects frame-semantic knowledge into Large Language Models via LoRA.
Outcome:	The proposed framework can generalize beyond surface cues in large language models.

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Xinyue Cui, Swabha Swayamdipta

Text Generation

Challenge:	despite the remarkable generative capabilities of language models, their effectiveness on explicit manipulation and generation of linguistic structures remains understudied.
Approach:	They propose a framework to generate frame-semantically annotated sentences following FrameNet . they use explicit semantic information to generate frames with high human acceptance .
Outcome:	The proposed framework produces frame-semantic annotations with high human acceptance . generating high-quality, semantically rich data is effective in low-resource settings, but not under higher resource settings.

Semantic Frame Induction from a Real-World Corpus (2025.acl-srw)

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Shogo Tsujimoto, Kosuke Yamada, Ryohei Sasano

Language Models

Challenge:	Existing studies on semantic frame induction have demonstrated that pre-trained language models (PLMs) have led to more accurate results.
Approach:	They conduct semantic frame induction using the Colossal Clean Crawled Corpus and assess the applicability of existing frame inducing methods to real-world data.
Outcome:	The proposed methods outperform existing methods on real-world data and can induce frames corresponding to novel concepts.

Frame Shift Prediction (2022.lrec-1)

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Zheng Xin Yong, Patrick D. Watson, Tiago Timponi Torrent, Oliver Czulo, Collin Baker

What just happened? Evaluating retrofitted distributional word vectors (N19-1)

Challenge:	Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames.
Approach:	They propose a task to predict cross-linguistic frame-to-frame correspondence and propose auxiliary training to learn cross-lingual frame-by-frame correlation.
Outcome:	The proposed task can learn cross-linguistic frame-to-frame correspondence and predict frame shifts in a Berkeley FrameNet-like configuration.

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Dmetri Hayes

Representation Learning

Challenge:	Recent work has attempted to enhance vector space representations using information from structured semantic resources.
Approach:	They propose a root-mean-square error evaluation metric to evaluate the utility of different lexical resources for retrofitting.
Outcome:	The proposed method improves word similarity performance by using root-mean-square error (RMSE) and root-macro-error (RMME) metric.

FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)

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Yucheng Li, Shun Wang, Chenghua Lin, Frank Guerin, Loic Barrault

Multimodal Frame Identification with Multilingual Evaluation (N18-1)

Challenge:	Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language.
Approach:	They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection.
Outcome:	The proposed model is more explainable and interpretable than existing models.

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Teresa Botschen, Iryna Gurevych, Jan-Christoph Klie, Hatem Mousselly-Sergieh, Stefan Roth

Multimodality

Challenge:	FrameNet Semantic Role Labeling aims to disambiguate situations around predicates using textual representations.
Approach:	They extend a frame identification task to leverage multimodal representations to improve FrameNet Semantic Role Labeling.
Outcome:	The proposed system outperforms its unimodal counterpart on the English frameNet and its German counterpart on IMAGINED words.

Semantic Frame Induction with Deep Metric Learning (2023.eacl-main)

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Kosuke Yamada, Ryohei Sasano, Koichi Takeda

Transfer of Frames from English FrameNet to Construct Chinese FrameNet: A Bilingual Corpus-Based Approach (L18-1)

Challenge:	Recent studies have shown the usefulness of contextualized word embeddings in semantic frame induction, but they are not always consistent with human intuitions about semantic frames.
Approach:	They propose a model that fine-tunes contextualized embeddings to perform semantic frame induction.
Outcome:	The proposed model improves clustering evaluation scores on FrameNet by 8 points or more.

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Tsung-Han Yang, Hen-Hsen Huang, An-Zi Yen, Hsin-Hsi Chen

Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)

Challenge:	Current publicly available Chinese FrameNet has a relatively low coverage of frames and lexical units compared with other languages.
Approach:	They propose an automatic way to construct Chinese FrameNet using a sentence-aligned English-Chinese bilingual corpus.
Outcome:	The proposed resource can provide frame recommendations acceptable by annotators.

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Gabriel Marzinotto, Jeremy Auguste, Frederic Bechet, Geraldine Damnati, Alexis Nasr

Language-based General Action Template for Reinforcement Learning Agents (2021.findings-acl)

Challenge:	a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project.
Approach:	They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation .
Outcome:	The proposed approach leverages the manual annotation of larger corpora than full text parsing.

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Ryosuke Kohita, Akifumi Wachi, Daiki Kimura, Subhajit Chaudhury, Michiaki Tatsubori, Asim Munawar

Challenge:	Prior knowledge is important in decision-making, and humans preserve it in the form of natural language (NL).
Approach:	They propose an environmentagnostic action framework that incorporates prior knowledge into decision-making . they propose to use general semantic schemes to facilitate agent in finding plausible actions .
Outcome:	The proposed agent performs better than agents that rely on gamespecific actions.

Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)

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Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li, Hu Zhang

GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles (2023.acl-long)

Challenge:	Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them.
Approach:	They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences.
Outcome:	The proposed model outperforms state-of-the-art methods on two popular benchmark datasets.

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Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang, Nanyun Peng

Exploiting Definitions for Frame Identification (2021.eacl-main)

Challenge:	Existing benchmarking datasets for Event Argument Extraction (EAE) cover less than 40 event types and 25 entity-centric argument roles.
Approach:	They propose to use a large and diverse EAE ontology to create a semantic role labeling dataset for EAE that incorporates 115 events and 220 argument roles.
Outcome:	The proposed ontology concludes with 115 events and 220 argument roles, with a significant portion of roles not being entities.

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Tianyu Jiang, Ellen Riloff

Synonymy in Bilingual Context: The CzEngClass Lexicon (C18-1)

Challenge:	a frame-semantic parsing task is to determine which frame best captures the meaning of a word or phrase in a sentence.
Approach:	They propose a frame identification model that generates representations for frames and lexical units (senses) they evaluate the model on three data sets and show it consistently achieves better performance than previous systems.
Outcome:	The proposed model consistently outperforms previous systems on three data sets.

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Zdeňka Urešová, Eva Fučíková, Eva Hajičová, Jan Hajič

Syntactic Text Processing

Challenge:	Existing lexical resources for semantic annotation of synonyms are lacking in computational language processing.
Approach:	They describe a bilingual lexical resource being built to investigate verbal synonymy in bilingual context and relate semantic roles common to one synonym class to verb arguments.
Outcome:	The proposed resource is based on English and Czech WordNet, FrameNet, PropBank, VerbNet (SemLink), and valency lexicons for Czech and English (PDT-Vallex, Vallex, and EngValleX).

A Crowdsourced Frame Disambiguation Corpus with Ambiguity (N19-1)

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Anca Dumitrache, Lora Aroyo, Chris Welty

Challenge:	Using crowdsourcing, we have found that inter-annotator disagreement is at least partly caused by ambiguity inherent to the text and frames.
Approach:	They propose a crowdsourcing approach to capture inter-annotator disagreement by a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word.
Outcome:	The proposed approach captures disagreement between the annotations of 1,000 word-sentence pairs and scores on the likelihood that each frame applies to the word.

FrameNet-assisted Noun Compound Interpretation (2021.findings-acl)

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Girishkumar Ponkiya, Diptesh Kanojia, Pushpak Bhattacharyya, Girish Palshikar

Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)

Challenge:	Existing methods for predicting semantic labels for noun compound interpretation are difficult.
Approach:	They propose to predict semantic labels in a continuous embedding space using FrameNet data.
Outcome:	The proposed method performs well on unseen labels, with 5% and 2% improvement over baselines for frame and FE prediction.

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Carl Edwards, Heng Ji

Modeling Factual Claims with Semantic Frames (2020.lrec-1)

Challenge:	Existing methods for event extraction use annotated event types but are expensive and time-consuming.
Approach:	They propose a semi-supervised approach to learning new event types using a masked contrastive loss.
Outcome:	The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch.

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Fatma Arslan, Josue Caraballo, Damian Jimenez, Chengkai Li

Challenge:	In recent years, the proliferation of misinformation has reached a staggering pace eroding people's confidence in politics and even affected democracies.
Approach:	They propose an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims.
Outcome:	The proposed extension provides 2,540 fully annotated sentences and can be used to understand how these frames are intended to work and to train machine learning models.

A Graph-Based Neural Model for End-to-End Frame Semantic Parsing (2021.emnlp-main)

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ZhiChao Lin, Yueheng Sun, Meishan Zhang

Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling (2020.emnlp-main)

Challenge:	Existing studies focus on frame semantic parsing as a graph construction problem.
Approach:	They propose an end-to-end neural model to tackle frame semantic parsing jointly.
Outcome:	The proposed model is highly competitive and performs better than pipeline models on two benchmark datasets.

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Diego Marcheggiani, Ivan Titov

A Double-Graph Based Framework for Frame Semantic Parsing (2022.naacl-main)

Challenge:	Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles.
Approach:	They propose to use graph convolutional networks to encode constituents and inform an SRL system by combining word representations of the first and last words in a constituent tree.
Outcome:	The proposed model is compared with other models and shows that it is more efficient than dependency trees.

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Ce Zheng, Xudong Chen, Runxin Xu, Baobao Chang

Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (2024.acl-long)

Challenge:	Frame semantic parsing is a fundamental NLP task, which consists of three subtasks: frame identification, argument identification and role classification.
Approach:	They propose a frame semantic parser with a double-graph to derive knowledge-enhanced representations for frames and FEs.
Outcome:	The proposed method outperforms the state-of-the-art method by up to 1.7 F1-score on two FrameNet datasets.

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Jacob Devasier, Yogesh Gurjar, Chengkai Li

A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier (L18-1)

Challenge:	Using a RoBERTa-based filter, we achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012.
Approach:	They propose a new prefix tree modification to enable robust support for multi-word lexical units and a RoBERTa-based filter to achieve an F1 score of 0.775.
Outcome:	The proposed model achieves an F1 score of 0.775, surpassing the state-of-the-art model by +0.012.

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Bolette Pedersen, Sanni Nimb, Anders Søgaard, Mareike Hartmann, Sussi Olsen

Information Retrieval

Challenge:	a Danish FrameNet is a lexicon based on the Danish Thesaurus . it is significantly faster than building a new one from scratch .
Approach:	They propose a way to efficiently compile a Danish FrameNet based on the Danish Thesaurus . they present the corresponding corpus annotations of frames and roles and show how this can be used for a semantic frame classifier .
Outcome:	The proposed approach is faster than building a lexicon from scratch.

Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction (2021.findings-acl)

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Kosuke Yamada, Ryohei Sasano, Koichi Takeda

Text Clustering

Challenge:	Contextualized word representations are effective in many natural language processing tasks, but it remains unclear to what extent they can cover hand-coded semantic information such as semantic frames.
Approach:	They compare contextualized word representations with two English frame-semantic resources . they find that several contextualized representations are informative for semantic frame induction .
Outcome:	The proposed representations are useful in natural language processing tasks, but are not fully understood by the literature.

Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame Induction (2020.lrec-1)

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Zheng Xin Yong, Tiago Timponi Torrent

Text Clustering

Challenge:	Empirical results show that definitions provide contextual information for representing and characterizing the frame membership of lexical units.
Approach:	They propose a two-step frame induction process to remove lexical units that cannot fit into existing frames in Berkeley FrameNet.
Outcome:	The proposed method outperforms state-of-the-art methods in both steps of the frame induction process.

FAMuS: Frames Across Multiple Sources (2024.naacl-long)

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Siddharth Vashishtha, Alexander Martin, William Gantt, Benjamin Van Durme, Aaron White

Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (2022.findings-emnlp)

Challenge:	Recent work in document-level event and argument extraction tasks suffer from two key shortcomings.
Approach:	They propose to combine Wikipedia passages with underlying, genre-diverse source articles for an event . they propose two key task enabled by FAMuS: source validation and cross-document argument extraction .
Outcome:	The proposed system can extract event arguments from document and report documents.

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Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng

Towards a Standardized Dataset for Noun Compound Interpretation (L18-1)

Challenge:	Existing methods for few-shot event detection are inaccurate and lack a prototype representation module.
Approach:	They propose a Knowledge-Enhanced self-supervised prototypical network for few-shot event detection . it adopts hybrid rules which align event types to FrameNet and introduces knowledge to obtain more instances .
Outcome:	The proposed network improves few-shot event detection performance on three benchmark datasets.

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Girishkumar Ponkiya, Kevin Patel, Pushpak Bhattacharyya, Girish K Palshikar

Explainability & Interpretability in NLP

Challenge:	Noun compounds are interesting constructs in Natural Language Processing . lack of standardized set of relation inventories and annotated datasets hinders interpretation .
Approach:	They propose a dataset that uses FrameNet as its semantic relation inventory to examine noun compounds.
Outcome:	The proposed dataset is linguistically grounded and uses FrameNet as its semantic relation inventory.

Learning Prototypical Functions for Physical Artifacts (2021.acl-long)

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Tianyu Jiang, Ellen Riloff

Challenge:	a new task is designed to learn the prototypical uses of human-made physical objects . human beings are creative, and they create things for a reason . humans often infer that the object will be used in the most prototypical way unless told otherwise .
Approach:	They propose a task to learn the prototypical uses for human-made physical objects . they use frames from FrameNet to represent a set of common functions for objects based on their prototypical function .
Outcome:	The proposed task uses masked patterns to model prototypical uses for objects . the proposed model predicts the prototypical functions of objects and can be used to make models .

Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction (2023.findings-acl)

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Kosuke Yamada, Ryohei Sasano, Koichi Takeda

EventOA: An Event Ontology Alignment Benchmark Based on FrameNet and Wikidata (2023.findings-acl)

Challenge:	Existing methods for semantic frame induction are labor intensive . a method that uses contextualized embeddings can be used to acquire frame element knowledge.
Approach:	They propose a method that applies deep metric learning to semantic frame induction tasks . they use a pre-trained language model to fine-tune frame-annotated models to perform argument clustering .
Outcome:	The proposed method achieves substantially better performance than existing methods on FrameNet.

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Shaoru Guo, Chenhao Wang, Yubo Chen, Kang Liu, Ru Li, Jun Zhao

Knowledge Representation

Challenge:	Existing studies on event ontologies focus on entity-based OA, and neglect event-based one . however, independent development of event ontoologies often results in heterogeneous representations that raise the need for establishing alignments between semantically related events.
Approach:	They propose a multi-view event ontology alignment method that utilizes description information and neighbor information to obtain richer representations of the event ontoologies.
Outcome:	The proposed method outperforms existing entity-based methods and can serve as a strong baseline for future research.

Frame2: A FrameNet-based Multimodal Dataset for Tackling Text-image Interactions in Video (2024.lrec-main)

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Frederico Belcavello, Tiago Timponi Torrent, Ely E. Matos, Adriana S. Pagano, Maucha Gamonal, Natalia Sigiliano, Lívia Vicente Dutra, Helen de Andrade Abreu, Mairon Samagaio, Mariane Carvalho, Franciany Campos, Gabrielly Azalim, Bruna Mazzei, Mateus Fonseca de Oliveira, Ana Carolina Loçasso Luz, Lívia Pádua Ruiz, Júlia Bellei, Amanda Pestana, Josiane Costa, Iasmin Rabelo, Anna Beatriz Silva, Raquel Roza, Mariana Souza, Igor Oliveira

Multimodality

Challenge:	et al., 2016) describe a multimodal dataset built from a Brazilian travel TV show . frameNet is composed of frames and their associated roles in a network of typed frame-to-frame relations.
Approach:	They present a multimodal dataset built from a Brazilian travel TV show annotated for FrameNet categories for both text and image communicative modes.
Outcome:	The proposed dataset includes 230 minutes of video annotated for FrameNet categories . the model can be applied to other communicative modes, i.e., images .

Definition Generation for Automatically Induced Semantic Frame (2024.findings-acl)

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Yi Han, Ryohei Sasano, Koichi Takeda

A Corpus for Visual Question Answering Annotated with Frame Semantic Information (2020.lrec-1)

Challenge:	Semantic frames are conceptual structures that describe specific types of situations or events.
Approach:	They propose to generate frame definitions from a set of frame-evoking words using a large language model.
Outcome:	The proposed task incorporates frame element reasoning as chain-of-thought to enhance the inclusion of correct frame elements in the generated definitions.

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Mehrdad Alizadeh, Barbara Di Eugenio

Visual Data in NLP

Challenge:	Visual Question Answering (VQA) is a computer vision problem.
Approach:	They propose to annotate a visual question answering dataset with verb semantics to help the model understand verbs.
Outcome:	The proposed system is built on the imSitu dataset annotated with verb semantic information.

Metaphor Suggestions based on a Semantic Metaphor Repository (L18-1)

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Gerard de Melo

Challenge:	Existing algorithms for suggesting metaphors have been used to find related words . corpus studies have found that metaphors are very pervasive even in formal language .
Approach:	They propose an algorithm that suggests metaphoric means of referring to concepts . they use MetaNet, a repository of conceptual metaphor, and lexical resources .
Outcome:	The proposed model expands the potential of the original repository by enabling new connections to be drawn.

Cross-lingual Linking of Automatically Constructed Frames and FrameNet (2022.lrec-1)

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Ryohei Sasano

Cross-Lingual Transfer

Challenge:	Existing semantic frame resources have been manually elaborated, but manual development is labor-intensive.
Approach:	They propose to link Japanese frames to English FrameNet by using cross-lingual word embeddings and a model that takes only the frame-evoking words into account.
Outcome:	The proposed model will facilitate the development of cross-lingual frame resources.

Creation of a Balanced State-of-the-Art Multilayer Corpus for NLU (L18-1)

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Normunds Gruzitis, Lauma Pretkalnina, Baiba Saulite, Laura Rituma, Gunta Nespore-Berzkalne, Arturs Znotins, Peteris Paikens

Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles (2020.lrec-1)

Challenge:	Using full stack of language resources, we are creating a balanced text corpus for Latvian.
Approach:	They propose to create a syntactically and semantically annotated multilayered corpus for Latvian . they use widely acknowledged and cross-lingual representations for the corpus .
Outcome:	The proposed corpus adopts widely recognized and cross-lingual representations for natural language understanding and generation in Latvian.

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Ben Eyal, Michael Elhadad

Identifying Physical Object Use in Sentences (2022.emnlp-main)

Challenge:	Existing semantic role labeling resources for Hebrew are not available in English.
Approach:	They propose a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English to Hebrew.
Outcome:	The proposed resource is built semi-automatically from an English dataset . it includes morphological analysis, dependency syntax and semantic role labeling .

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Tianyu Jiang, Ellen Riloff

Text Classification

Challenge:	Prior research has focused on learning the prototypical functions of physical objects . but many sentences refer to objects even when they are not used .
Approach:	They propose a task that determines whether a physical object mentioned in a sentence was used or likely will be used.
Outcome:	The proposed model exploits data augmentation methods and FrameNet to fine-tune a pre-trainedmodel.

NutFrame: Frame-based Conceptual Structure Induction with LLMs (2024.lrec-main)

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Shaoru Guo, Yubo Chen, Kang Liu, Ru Li, Jun Zhao

Challenge:	Existing studies focus on syntactic knowledge and world knowledge, but conceptual structure is not well-understood.
Approach:	They propose a benchmark for coNceptual structure induction based on FrameNet . they use prompts to induce conceptual structure of Framenet with LLMs .
Outcome:	The proposed model is able to induce conceptual structure of FrameNet with LLMs.

Towards Standardized Annotation and Parsing for Korean FrameNet (2024.lrec-main)

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Yige Chen, Jae Ihn, KyungTae Lim, Jungyeul Park

Syntactic Text Processing

Challenge:	Existing studies on Korean FrameNet have focused on English, but annotations are not optimally designed for Korean.
Approach:	They propose a morphologically enhanced annotation strategy for Korean FrameNet datasets and parsing by leveraging the CoNLL-U format.
Outcome:	The proposed method improves the annotation accuracy of Korean FrameNet datasets and their parsers.

Transformer-based Swedish Semantic Role Labeling through Transfer Learning (2024.lrec-main)

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Dana Dannélls, Richard Johansson, Lucy Yang Buhr

Do LLMs Encode Frame Semantics? Evidence from Frame Identification (2025.emnlp-main)

Challenge:	Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence.
Approach:	They propose to build a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource.
Outcome:	The proposed model outperforms two different cross-lingual transfer models and shows that the multilingual learning outperformed the other models.

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Jayanth Krishna Chundru, Rudrashis Poddar, Jie Cao, Tianyu Jiang