SEMANTIC ROLE LABELING

Graph-based Deep Learning in Natural Language Processing (D19-2)

Challenge:	Existing studies show that syntactic information is useful for a wide variety of NLP tasks.
Approach:	They propose to use word-level representations to learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision.
Outcome:	The proposed model encodes significant amounts of syntax even without explicit supervision.

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Shikhar Vashishth, Naganand Yadati, Partha Talukdar

Multimodality

Challenge:	This tutorial aims to introduce graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP)
Approach:	It provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP).
Outcome:	This tutorial provides a brief introduction to graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for natural language processing (NLP).

N-LTP: An Open-source Neural Language Technology Platform for Chinese (2021.emnlp-demo)

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Wanxiang Che, Yunlong Feng, Libo Qin, Ting Liu

Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)

Challenge:	Existing tools that teach an independent model for each task are not supported in Chinese.
Approach:	They propose an open-source neural language platform supporting six Chinese NLP tasks . source code, documentation, and pre-trained models are available at https://github.com/hit-SCIR/ltp .
Outcome:	The proposed platform supports six Chinese NLP tasks.

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Hai Zhao, Rui Wang, Kehai Chen

Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets (P18-3)

Challenge:	tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks.
Approach:	tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks.
Outcome:	This tutorial will introduce the background and the latest progress of syntactic parsing and SRL/NMT.

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Kushagra Singh, Indira Sen, Ponnurangam Kumaraguru

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) is an important text analysis task . code-mixing occurs when lexical items and grammatical features from two languages appear in one sentence .
Approach:	They propose to use language identifiers, parts-of-speech tags and chunkers to analyze code-mixed data.
Outcome:	The proposed method outperforms the best baseline by 33.18%.

Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)

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Han Wu, Haochen Tan, Kun Xu, Shuqi Liu, Lianwei Wu, Linqi Song

PropBank Comes of Age—Larger, Smarter, and more Diverse (2022.starsem-1)

Challenge:	Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.
Approach:	They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives.
Outcome:	The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora .

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Sameer Pradhan, Julia Bonn, Skatje Myers, Kathryn Conger, Tim O’gorman, James Gung, Kristin Wright-bettner, Martha Palmer

Leveraging Active Learning to Minimise SRL Annotation Across Corpora (2023.starsem-1)

Challenge:	The PropBank has been used for semantic role labeling for over 20 years . it includes non-verbal predicates, adjectives, prepositions and multi-word expressions .
Approach:	They describe the evolution of the PropBank approach to semantic role labeling over the last 20 years . they describe the substantial effort that has gone into ensuring consistency and reliability of the various annotated datasets and resources .
Outcome:	The PropBank has been used for more than 20 years to test semantic role labeling systems.

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Skatje Myers, Martha Palmer

Character-Level Models versus Morphology in Semantic Role Labeling (P18-1)

Challenge:	In this paper, we investigate the application of active learning to semantic role labeling (SRL) using Bayesian Active Learning by Disagreement (BALD).
Approach:	They propose a sentence-focused selection method that is based off of previous methods of using model dropout to approximate a Gaussian process for SRL.
Outcome:	The proposed selection method improves on three different domain corpora on three domains with a large and diverse corpus.

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Gözde Gül Şahin, Mark Steedman

An Analysis under a Unified Formulation of Learning Algorithms with Output Constraints (2024.acl-srw)

Challenge:	Character-level models are used for high-level semantic analysis tasks such as semantic role labeling.
Approach:	They train character-level models that use word, character and morphology level information . they analyze how performance of characters compare to words and a variety of morphological typologies .
Outcome:	The results shed light on important characteristics of character-level models and their semantic capability.

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Mooho Song, Jay-Yoon Lee

Challenge:	Existing work on NN models with output constraints has not been able to categorize them in a unified manner.
Approach:	They propose new algorithms to integrate the information of main task and constraint injection . they use the H-score as a metric for considering main task metric and constrain infringement simultaneously .
Outcome:	The proposed algorithms integrate the information of main task and constraint injection, inspired by continual-learning algorithms.

Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling (2021.findings-acl)

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Hao Fei, Shengqiong Wu, Yafeng Ren, Fei Li, Donghong Ji

Syntax-Enhanced Self-Attention-Based Semantic Role Labeling (D19-1)

Challenge:	Existing studies use only one singleton syntax for semantic role labeling (SRL).
Approach:	They propose a TreeLSTM-based integration that integrates phrasal boundaries and semantic relations from dependency into a labelaware GCN solution for simultaneously modeling syntactic edges and labels.
Outcome:	The proposed system achieves state-of-the-art performance on span-based and dependency-based SRL.

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Yue Zhang, Rui Wang, Luo Si

A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling (2024.lrec-main)

Challenge:	Abstract: Syntax is the bridge to semantics, but recent studies have discussed the necessity of syntax in the context of SRL.
Approach:	They propose a syntax-enhanced self-attention model that incorporates syntactic knowledge into the SRL task effectively.
Outcome:	The proposed model achieves state-of-the-art for the Chinese SRL task on the CoNLL-2009 dataset.

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Yige Chen, KyungTae Lim, Jungyeul Park

Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling (N19-1)

Challenge:	Semantic role labeling is an essential component of semantic and syntactic processing of natural languages.
Approach:	They propose an annotation strategy for Korean semantic role labeling that is in line with the previously proposed linguistic theories as well as the distinct properties of the Korean language.
Outcome:	The proposed annotation strategy is consistent with the proposed linguistic theories and the distinct properties of the Korean language.

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Meishan Zhang, Peili Liang, Guohong Fu

DeepStruct: Pretraining of Language Models for Structure Prediction (2022.findings-acl)

Challenge:	Existing work on opinion role labeling (ORL) is highly correlative with semantic role labeled (SRL) SRL is used to identify opinion holders and holder expressions for a given predicate.
Approach:	They propose a method to enhance opinion role labeling by presenting semantic-aware word representations which are learned from SRL.
Outcome:	The proposed method outperforms two other methods on a benchmark MPQA corpus and achieves higher F scores.

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Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song

Syntax-aware Neural Semantic Role Labeling with Supertags (N19-1)

Challenge:	Pretrained language models perform structural understanding tasks that focus on understanding one aspect of the text.
Approach:	They propose a method for improving the structural understanding abilities of language models by pretraining them to generate structures from the text on task-agnostic corpora.
Outcome:	The proposed model performs state-of-the-art on 21 of 28 datasets.

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Jungo Kasai, Dan Friedman, Robert Frank, Dragomir Radev, Owen Rambow

Semi-Supervised Semantic Role Labeling with Cross-View Training (D19-1)

Challenge:	a new syntax-aware model for dependency-based semantic role labeling outperforms syntax-based models for English and Spanish.
Approach:	They propose a syntax-aware model for dependency-based semantic role labeling that outperforms syntax-based models for English and Spanish.
Outcome:	The proposed model outperforms syntax-agnostic models for English and Spanish.

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Rui Cai, Mirella Lapata

The Role of Semantic Parsing in Understanding Procedural Text (2023.findings-eacl)

Challenge:	Recent approaches rely on expensive annotations and are unavailable in low resource scenarios (e.g., rare languages or domains).
Approach:	They propose an end-to-end SRL model which leverages unlabeled data and propose to reduce the annotation effort involved via semi-supervised learning.
Outcome:	The proposed model outperforms the state-of-the-art in English and consistently improves performance in other languages, including Chinese, German, and Spanish.

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Hossein Rajaby Faghihi, Parisa Kordjamshidi, Choh Man Teng, James Allen

Extracting Victim Counts from Text (2023.eacl-main)

Challenge:	Inferring actions and their impact on entities involved in a procedural text can be challenging in various aspects.
Approach:	They propose a symbolic parser and semantic role labeling as two sources of semantic parsing knowledge.
Outcome:	The proposed framework integrates semantic parsing knowledge into state-of-the-art neural models and shows that it improves procedural understanding.

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Mian Zhong, Shehzaad Dhuliawala, Niklas Stoehr

Tagging

Challenge:	Using tagging and regex methods, data on injured, displaced, or abused victims is difficult . data on earthquake injuries and deaths is scarce, subjective, or biased .
Approach:	They compare tagging approaches to extract injured, displaced, or abused victims . they discuss calibration and investigate out-of-distribution and few-shot performance .
Outcome:	The proposed model is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact.

Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim? (2023.eacl-main)

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Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, Tanmoy Chakraborty

Visual Data in NLP

Challenge:	A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
Approach:	They propose to use a memes dataset on US Politics and Covid-19 memes to characterize the role of harmful entities in memes.
Outcome:	The proposed model improves 4% over baseline and 1% over competing models.

End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)

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Ishani Mondal, Yufang Hou, Charles Jochim

Knowledge Representation

Challenge:	a new schema for NLP knowledge about tasks, datasets and metrics is proposed.
Approach:	They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain.
Outcome:	The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph .

Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying (2022.emnlp-main)

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Zhisong Zhang, Emma Strubell, Eduard Hovy

A Structured Span Selector (2022.naacl-main)

Challenge:	Existing methods for event argument extraction are limited by the limited amount of annotations available.
Approach:	They propose to use SRL annotations for event argument extraction . they propose to specify natural language-like queries to tackle label mismatch problem .
Outcome:	The proposed model achieves impressive zero-shot results on English benchmarks . it also provides benefits in low-resource cases, where few annotations are available .

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Tianyu Liu, Yuchen Jiang, Ryan Cotterell, Mrinmaya Sachan

A Span Selection Model for Semantic Role Labeling (D18-1)

Challenge:	a typical approach to natural language processing tasks involves selecting text spans and making decisions about them.
Approach:	They propose a grammar-based structured span selection model which learns to make use of partial span annotations.
Outcome:	The proposed model improves on two popular span prediction tasks.

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Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto

Generalizing Natural Language Analysis through Span-relation Representations (2020.acl-main)

Challenge:	Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak .
Approach:	They propose a span-based model that takes into account all possible argument spans and scores them for each label.
Outcome:	The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets.

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Zhengbao Jiang, Wei Xu, Jun Araki, Graham Neubig

Representation Learning

Challenge:	a large number of natural language processing tasks are generated with specially designed architectures.
Approach:	They propose to represent a wide variety of tasks in a single unified format . they perform extensive experiments to demonstrate benefits of multi-task learning .
Outcome:	The proposed model performs comparable to state-of-the-art models on 10 tasks . it also shows that it can analyze differences and similarities in how the model handles different tasks compared to other models .

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

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

Question Generation Using Sequence-to-Sequence Model with Semantic Role Labels (2023.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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Alireza Naeiji, Aijun An, Heidar Davoudi, Marjan Delpisheh, Muath Alzghool

When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context (2024.findings-emnlp)

Challenge:	Existing question generation methods that generate multiple questions from text are labor-intensive and do not capture the complexity of ways a human asks questions.
Approach:	They propose a question generation method that combines the benefits of rule-based and neural sequence-to-sequence (Seq2Sequen) models.
Outcome:	The proposed method significantly improves the state-of-the-art neural question generation approaches on three real-world data sets.

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Enrique Noriega-Atala, Robert Vacareanu, Salena Ashton, Adarsh Pyarelal, Clayton Morrison, Mihai Surdeanu

Do Syntax Trees Help Pre-trained Transformers Extract Information? (2021.eacl-main)

Challenge:	a relatively small fine-tuned encoder-decoder model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information.
Approach:	They propose a neural architecture finetuned for the task of scenario context generation . they use a curated dataset of time and location annotations to train an encoder-decoder architecture .
Outcome:	The proposed model performs better than out-of-the-box LLMs and semantic role labeling parsers to accurately predict the relevant scenario information of a particular entity or event.

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Devendra Sachan, Yuhao Zhang, Peng Qi, William L. Hamilton

CANarEx: Contextually Aware Narrative Extraction for Semantically Rich Text-as-data Applications (2022.findings-emnlp)

Challenge:	Recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models.
Approach:	They propose to incorporate dependency tree information into pre-trained transformers for three tasks . they propose a late fusion approach and a joint fusion technique to infuses syntax structure into attention layers.
Outcome:	The proposed models obtain state-of-the-art results on SRL and relation extraction tasks.

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Nandini Anantharama, Simon Angus, Lachlan O’Neill

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

Challenge:	Narrative modelling is a field of active research that conceptualizes narratives as connected entity chains.
Approach:	They propose an alternative narrative extraction approach using semantic role labeling to extract tuples from text, then dimensionality reduction to reduce the space of entities and connections separately.
Outcome:	The proposed approach improves on a text-as-data task and improves accuracy and recall.

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ZhiChao Lin, Yueheng Sun, Meishan Zhang

Using Semantic Role Labeling to Improve Neural Machine Translation (2022.lrec-1)

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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Reinhard Rapp

Multilinguality

Challenge:	despite progress in machine translation, some form of language understanding may be desirable . current systems rely on pattern recognition, but some form may be useful .
Approach:	They use semantic role labeling to annotate a standard parallel corpus with semantic roles . they then train a neural machine translation system using the annotated corpus and original unannotated text .
Outcome:	The proposed system improves BLEU scores for English, French, German, Greek and Spanish.

Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)

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Chenyang Lyu, Lifeng Shang, Yvette Graham, Jennifer Foster, Xin Jiang, Qun Liu

Text Generation

Challenge:	Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers.
Approach:	They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system.
Outcome:	The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones.

Polyglot Contextual Representations Improve Crosslingual Transfer (N19-1)

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Phoebe Mulcaire, Jungo Kasai, Noah A. Smith

Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization (2023.findings-acl)

Challenge:	Existing methods for crosslingual transfer use multilingual word embeddings, but contextual word representations are not yet available.
Approach:	They propose a method to produce multilingual contextual word representations by training a single language model on text from multiple languages.
Outcome:	The proposed method compares model models to monolingual and non-contextual variants and shows that polyglot learning can be beneficial for multilingual representations.

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Hou Pong Chan, Qi Zeng, Heng Ji

Explainability & Interpretability in NLP

Challenge:	Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarizing systems.
Approach:	They propose a task of fine-grained inconsistency detection to detect inconsistent summaries . they represent facts with semantic frames extracted by semantic role labeling and highlight related semantic frames to predict inconsistance .
Outcome:	The proposed model outperforms baseline models and provides evidence to support or refute the summary.

Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data (D18-1)

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Zi Lin, Yuguang Duan, Yuanyuan Zhao, Weiwei Sun, Xiaojun Wan

Auditing Deep Learning processes through Kernel-based Explanatory Models (D19-1)

Challenge:	a learner language (interlanguage) is an idiolect developed by a learning of a second or foreign language.
Approach:	They propose to use semantic role labeling as a case task to parse interlanguages . they then evaluate three off-the-shelf SRL systems to gauge how successful they are .
Outcome:	The proposed model achieves an F-score of 72.06, a 2.02 point improvement over the baseline.

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Danilo Croce, Daniele Rossini, Roberto Basili

An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks (2020.emnlp-main)

Challenge:	Existing nonlinearity of deep learning models can be a major drawback . ethical accountability of such systems is becoming a crucial issue .
Approach:	They propose to use Layerwise Relevance Propagation to trace back connections between linguistic properties of input instances and system decisions.
Outcome:	The proposed model evaluates the transparency and coherence of analogy-based explanations modeling an audit stage for the system.

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Lifu Tu, Tianyu Liu, Kevin Gimpel

Challenge:	Recent work shows that conditional random fields (CRFs) perform well in sequence labeling tasks.
Approach:	They propose several high-order energy terms to capture dependencies among labels in sequence labeling . they use convolutional, recurrent, and self-attention networks to construct these energy terms .
Outcome:	The proposed approach improves on four sequence labeling tasks while having the same decoding speed as simple classifiers.

Finding a Balanced Degree of Automation for Summary Evaluation (2021.emnlp-main)

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Shiyue Zhang, Mohit Bansal

Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (2020.emnlp-main)

Challenge:	Automated summarization metrics are reliable but often poorly correlated with human judgment.
Approach:	They propose a semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method.
Outcome:	The proposed metrics are semi-automatic to automatic summary evaluation metrics, following the Pyramid human evaluation method.

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Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, Dong Yu

Paraphrasing

Challenge:	Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words.
Approach:	They propose to use semantic role labeling to provide additional guidance for multi-turn dialogue rewriting models.
Outcome:	The proposed model outperforms existing models on multi-turn dialogue rewriting tasks.

Syntax-aware Multilingual Semantic Role Labeling (D19-1)

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Shexia He, Zuchao Li, Hai Zhao

Linguistically-Informed Self-Attention for Semantic Role Labeling (D18-1)

Challenge:	Existing work on semantic role labeling (SRL) on English has focused on syntactic integration and enhanced word representation.
Approach:	They propose a method guided by syntactic rule to prune arguments to integrate syntax into multilingual SRL model simply and effectively.
Outcome:	The proposed model achieves state-of-the-art results on the CoNLL-2009 benchmarks of all seven languages.

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Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Existing models of semantic role labeling use no explicit linguistic features. prior work has shown that syntax trees can dramatically improve SRL decoding.
Approach:	They propose a neural network model that incorporates syntax using only raw tokens . they show that LISA out-performs the state-of-the-art with contextually-encoded word representations a 1.0 F1 on newswire and 2.0 F1 in out-of domain text .
Outcome:	The proposed model outperforms the state-of-the-art model with word embeddings and predicted predicates.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Reasoning

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)

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Tianze Shi, Igor Malioutov, Ozan Irsoy

On the Role of Semantic Proto-roles in Semantic Analysis: What do LLMs know about agency? (2025.findings-acl)

Challenge:	Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing.
Approach:	They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format.
Outcome:	The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing.

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Elizabeth Spaulding, Shafiuddin Rehan Ahmed, James Martin

Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus (2020.acl-main)

Challenge:	Existing studies on large language models (LLMs) have not explored their capacity to reason over event structure . et al., 2015, 142: e007-e0027; eugene, 1985; Weiner, 1995; saab, 1985) focus on the role of large language model in decision-making .
Approach:	They propose to characterize agents via properties such as "instigation" and "volition" they also examine whether incorporating semantic proto-role labeling context improves SRL performance .
Outcome:	The proposed model improves in a zero-shot setting by incorporating proto-role labeling context . the results support previous work showing that LLMs underperform human annotators in complex semantic analysis.

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Hao Fei, Meishan Zhang, Donghong Ji

Multilinguality

Challenge:	Existing approaches to semantic role labeling (SRL) are focusing on the English language.
Approach:	They propose a method for semantic role labeling that uses corpus translation to build training datasets from SRL annotations.
Outcome:	The proposed method is highly effective and can improve the target-language performance significantly.

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

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Ben Eyal, Michael Elhadad

Sarah Moeller, Irina Wagner, Martha Palmer, Kathryn Conger, Skatje Myers

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 .

The Russian PropBank (2020.lrec-1)

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Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)

Challenge:	Using proposition bank for Russian, we can automatically project semantic role labels from English to Russian.
Approach:	They propose a proposition bank for Russian that automatically projects semantic role labels from English to Russian.
Outcome:	The proposed resource automatically projectes semantic role labels from English to Russian.

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Yuanhe Tian, Han Qin, Fei Xia, Yan Song

Semantic Role Labeling Guided Out-of-distribution Detection (2024.lrec-main)

Challenge:	Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL .
Approach:	They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits.
Outcome:	The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets.

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Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi