Challenge: DISRPT is a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing.
Approach: They present a multilingual, multi-domain, and cross-framework benchmark dataset for discourse processing that includes 13 languages and 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks.
Outcome: The DISRPT dataset includes data from 24 corpora covering about 4 millions tokens and around 250,000 discourse relation instances from 4 discourse frameworks.

Similar Papers

CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations (2025.findings-acl)

Copied to clipboard

Challenge: Discourse parsing datasets based on conversations are restricted to a single domain . a lack of discourse structures in audio-based conversations is a challenge .
Approach: They introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse parsing in conversations.
Outcome: The proposed corpus is code-mixed in Hindi and English and annotated with nine discourse relations.
Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)

Copied to clipboard

Challenge: Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs).
Approach: They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations.
Outcome: Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages.
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)

Copied to clipboard

Challenge: Existing multilingual benchmarks focus primarily on language understanding tasks.
Approach: They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages.
Outcome: Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve.
GlobalBench: A Benchmark for Global Progress in Natural Language Processing (2023.emnlp-main)

Copied to clipboard

Challenge: despite advances in NLP, significant disparities in performance across languages still exist . prior benchmarks focused on a limited number of tasks and languages, but now GlobalBench tracks progress on all languages.
Approach: They propose to use global benchmarks to track progress on all NLP datasets in all languages.
Outcome: a new tool tracks progress on all NLP datasets in all languages and tracks per-speaker utility and equity . globalbench is designed to identify the most under-served languages and reward research efforts . a globalbech is available at https://github.com/neulab/globalbench.
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)

Copied to clipboard

Challenge: Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old.
Approach: They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing.
Outcome: The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain.
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to building cross-lingual summarization systems on dialogue documents are limited.
Approach: They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents.
Outcome: The proposed model outperforms pipeline models on ClidSum and mDialBART.
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset (2026.findings-acl)

Copied to clipboard

Challenge: Existing studies on multi-party dialogue discourse parsing focus on textual modality and two-party dialog . et al., 2016) focused on text-based discourse parses, ignoring the complexity and richness of multimodal interactions in real-world scenarios.
Approach: They construct the first publicly available English multimodal dataset for multi-party dialogue discourse parsing based on American TV dramas.
Outcome: The proposed dataset contains 495 dialogue segments with 6,374 utterances and 9.1 hours of parallel video content, covering rich multi-party interaction scenarios.
CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums (D19-55)

Copied to clipboard

Challenge: a dataset of written multilingual productions is released to explore the sociolinguistic underpinnings of written code-switching .
Approach: They use a reddit discussion platform to collect written code-switched productions . they examine whether oral code-witching findings are carried over to written code .
Outcome: The proposed dataset can facilitate a range of research and practical activities.
CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums (D19-1)

Copied to clipboard

Challenge: a dataset of written code-switched productions is curated from topical threads of multiple bilingual communities on the Reddit discussion platform.
Approach: They analyze a dataset of written code-switched productions curated from multiple bilingual communities on the reddit discussion platform and examine whether findings are carried over to written codeswitching in discussion forums.
Outcome: The proposed dataset can facilitate a range of research and practical activities.
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling (2021.findings-emnlp)

Copied to clipboard

Challenge: Multilingual and cross-lingual Semantic Role Labeling (SRL) has attracted increasing attention as multilingual text representation techniques have become more effective and widely available.
Approach: They propose a benchmark for multilingual and cross-lingual, span- and dependency-based SRL that provides expert-curated parallel annotations using a common predicate-argument structure inventory.
Outcome: The proposed benchmark provides expert-curated parallel annotations using a common predicate-argument structure inventory, allowing direct comparisons across languages and encouraging studies on cross-lingual transfer in SRL.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations