1 code implementation • 15 Mar 2024 • Xinrun Xu, Yuxin Wang, Chaoyi Xu, Ziluo Ding, Jiechuan Jiang, Zhiming Ding, Börje F. Karlsson
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry.
2 code implementations • 5 Mar 2024 • Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, Yifei Bi, Pengjie Gu, Xinrun Wang, Börje F. Karlsson, Bo An, Zongqing Lu
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
no code implementations • 17 Feb 2024 • Jian Wu, Linyi Yang, Yuliang Ji, Wenhao Huang, Börje F. Karlsson, Manabu Okumura
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts.
no code implementations • 9 Feb 2024 • Shivalika Singh, Freddie Vargus, Daniel Dsouza, Börje F. Karlsson, Abinaya Mahendiran, Wei-Yin Ko, Herumb Shandilya, Jay Patel, Deividas Mataciunas, Laura OMahony, Mike Zhang, Ramith Hettiarachchi, Joseph Wilson, Marina Machado, Luisa Souza Moura, Dominik Krzemiński, Hakimeh Fadaei, Irem Ergün, Ifeoma Okoh, Aisha Alaagib, Oshan Mudannayake, Zaid Alyafeai, Vu Minh Chien, Sebastian Ruder, Surya Guthikonda, Emad A. Alghamdi, Sebastian Gehrmann, Niklas Muennighoff, Max Bartolo, Julia Kreutzer, Ahmet Üstün, Marzieh Fadaee, Sara Hooker
The Aya initiative also serves as a valuable case study in participatory research, involving collaborators from 119 countries.
1 code implementation • arXiv 2023 • Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riabi, Yuval Pinter
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages.
Ranked #1 on Named Entity Recognition (NER) on UNER v1 (Danish)
no code implementations • 14 Nov 2023 • Yuhan Li, Jian Wu, Zhiwei Yu, Börje F. Karlsson, Wei Shen, Manabu Okumura, Chin-Yew Lin
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
1 code implementation • 29 Sep 2023 • Guangyao Chen, Siwei Dong, Yu Shu, Ge Zhang, Jaward Sesay, Börje F. Karlsson, Jie Fu, Yemin Shi
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
no code implementations • 24 May 2023 • Jian Wu, Yicheng Xu, Yan Gao, Jian-Guang Lou, Börje F. Karlsson, Manabu Okumura
A common challenge in HQA and other passage-table QA datasets is that it is generally unrealistic to iterate over all table rows, columns, and linked passages to retrieve evidence.
1 code implementation • 24 May 2023 • Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje F. Karlsson, Tiejun Zhao, Chin-Yew Lin
Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language.
1 code implementation • 31 Mar 2023 • Sanxing Chen, Yongqiang Chen, Börje F. Karlsson
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks.
no code implementations • 9 Dec 2022 • Yuxin Wang, Jieru Lin, Zhiwei Yu, Wei Hu, Börje F. Karlsson
Storytelling and narrative are fundamental to human experience, intertwined with our social and cultural engagement.
1 code implementation • 21 Nov 2022 • Qianhui Wu, Huiqiang Jiang, Haonan Yin, Börje F. Karlsson, Chin-Yew Lin
Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples.
2 code implementations • 24 Oct 2022 • Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
no code implementations • 20 Jul 2021 • Huiqiang Jiang, Guoxin Wang, WEILE CHEN, Chengxi Zhang, Börje F. Karlsson
While named entity recognition (NER) is a key task in natural language processing, most approaches only target flat entities, ignoring nested structures which are common in many scenarios.
Ranked #1 on Nested Mention Recognition on ACE 2005
1 code implementation • ACL 2021 • WEILE CHEN, Huiqiang Jiang, Qianhui Wu, Börje F. Karlsson, Yi Guan
Neural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages.
1 code implementation • 15 Jul 2020 • Qianhui Wu, Zijia Lin, Börje F. Karlsson, Biqing Huang, Jian-Guang Lou
Prior works in cross-lingual named entity recognition (NER) with no/little labeled data fall into two primary categories: model transfer based and data transfer based methods.
Ranked #1 on Cross-Lingual NER on NoDaLiDa Norwegian Bokmål
1 code implementation • ACL 2020 • Qianhui Wu, Zijia Lin, Börje F. Karlsson, Jian-Guang Lou, Biqing Huang
However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.
Ranked #1 on Cross-Lingual NER on CoNLL German
1 code implementation • 14 Nov 2019 • Qianhui Wu, Zijia Lin, Guoxin Wang, Hui Chen, Börje F. Karlsson, Biqing Huang, Chin-Yew Lin
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on Cross-Lingual NER on MSRA
1 code implementation • NAACL 2019 • Yuying Zhu, Guoxin Wang, Börje F. Karlsson
Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters.
Ranked #1 on Chinese Named Entity Recognition on Weibo NER (Accuracy-NE metric)
Chinese Named Entity Recognition named-entity-recognition +4