Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tan, Fiona Anting"'
Autor:
Tan, Fiona Anting, Yeo, Gerard Christopher, Jaidka, Kokil, Wu, Fanyou, Xu, Weijie, Jain, Vinija, Chadha, Aman, Liu, Yang, Ng, See-Kiong
The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with social-cognitive reasoning, a domain where humans naturally excel. Th
Externí odkaz:
http://arxiv.org/abs/2403.02246
Autor:
Fang, Xi, Xu, Weijie, Tan, Fiona Anting, Zhang, Jiani, Hu, Ziqing, Qi, Yanjun, Nickleach, Scott, Socolinsky, Diego, Sengamedu, Srinivasan, Faloutsos, Christos
Publikováno v:
TMLR 2024
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Eac
Externí odkaz:
http://arxiv.org/abs/2402.17944
Autor:
Hürriyetoğlu, Ali, Tanev, Hristo, Mutlu, Osman, Thapa, Surendrabikram, Tan, Fiona Anting, Yörük, Erdem
We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023. The workshop consists of regular papers, three keynotes, working papers of shared task participants, and shared task overview papers. This worksho
Externí odkaz:
http://arxiv.org/abs/2312.01244
Many financial jobs rely on news to learn about causal events in the past and present, to make informed decisions and predictions about the future. With the ever-increasing amount of news available online, there is a need to automate the extraction o
Externí odkaz:
http://arxiv.org/abs/2305.09359
Autor:
Tan, Fiona Anting, Hettiarachchi, Hansi, Hürriyetoğlu, Ali, Caselli, Tommaso, Uca, Onur, Liza, Farhana Ferdousi, Oostdijk, Nelleke
The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classificatio
Externí odkaz:
http://arxiv.org/abs/2211.12154
Current causal text mining datasets vary in objectives, data coverage, and annotation schemes. These inconsistent efforts prevent modeling capabilities and fair comparisons of model performance. Furthermore, few datasets include cause-effect span ann
Externí odkaz:
http://arxiv.org/abs/2208.09163
Autor:
Tan, Fiona Anting, Hürriyetoğlu, Ali, Caselli, Tommaso, Oostdijk, Nelleke, Nomoto, Tadashi, Hettiarachchi, Hansi, Ameer, Iqra, Uca, Onur, Liza, Farhana Ferdousi, Hu, Tiancheng
Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that focus more on linguistics. Ma
Externí odkaz:
http://arxiv.org/abs/2204.11714
Autor:
Dhole, Kaustubh D., Gangal, Varun, Gehrmann, Sebastian, Gupta, Aadesh, Li, Zhenhao, Mahamood, Saad, Mahendiran, Abinaya, Mille, Simon, Shrivastava, Ashish, Tan, Samson, Wu, Tongshuang, Sohl-Dickstein, Jascha, Choi, Jinho D., Hovy, Eduard, Dusek, Ondrej, Ruder, Sebastian, Anand, Sajant, Aneja, Nagender, Banjade, Rabin, Barthe, Lisa, Behnke, Hanna, Berlot-Attwell, Ian, Boyle, Connor, Brun, Caroline, Cabezudo, Marco Antonio Sobrevilla, Cahyawijaya, Samuel, Chapuis, Emile, Che, Wanxiang, Choudhary, Mukund, Clauss, Christian, Colombo, Pierre, Cornell, Filip, Dagan, Gautier, Das, Mayukh, Dixit, Tanay, Dopierre, Thomas, Dray, Paul-Alexis, Dubey, Suchitra, Ekeinhor, Tatiana, Di Giovanni, Marco, Goyal, Tanya, Gupta, Rishabh, Hamla, Louanes, Han, Sang, Harel-Canada, Fabrice, Honore, Antoine, Jindal, Ishan, Joniak, Przemyslaw K., Kleyko, Denis, Kovatchev, Venelin, Krishna, Kalpesh, Kumar, Ashutosh, Langer, Stefan, Lee, Seungjae Ryan, Levinson, Corey James, Liang, Hualou, Liang, Kaizhao, Liu, Zhexiong, Lukyanenko, Andrey, Marivate, Vukosi, de Melo, Gerard, Meoni, Simon, Meyer, Maxime, Mir, Afnan, Moosavi, Nafise Sadat, Muennighoff, Niklas, Mun, Timothy Sum Hon, Murray, Kenton, Namysl, Marcin, Obedkova, Maria, Oli, Priti, Pasricha, Nivranshu, Pfister, Jan, Plant, Richard, Prabhu, Vinay, Pais, Vasile, Qin, Libo, Raji, Shahab, Rajpoot, Pawan Kumar, Raunak, Vikas, Rinberg, Roy, Roberts, Nicolas, Rodriguez, Juan Diego, Roux, Claude, S., Vasconcellos P. H., Sai, Ananya B., Schmidt, Robin M., Scialom, Thomas, Sefara, Tshephisho, Shamsi, Saqib N., Shen, Xudong, Shi, Haoyue, Shi, Yiwen, Shvets, Anna, Siegel, Nick, Sileo, Damien, Simon, Jamie, Singh, Chandan, Sitelew, Roman, Soni, Priyank, Sorensen, Taylor, Soto, William, Srivastava, Aman, Srivatsa, KV Aditya, Sun, Tony, T, Mukund Varma, Tabassum, A, Tan, Fiona Anting, Teehan, Ryan, Tiwari, Mo, Tolkiehn, Marie, Wang, Athena, Wang, Zijian, Wang, Gloria, Wang, Zijie J., Wei, Fuxuan, Wilie, Bryan, Winata, Genta Indra, Wu, Xinyi, Wydmański, Witold, Xie, Tianbao, Yaseen, Usama, Yee, Michael A., Zhang, Jing, Zhang, Yue
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python
Externí odkaz:
http://arxiv.org/abs/2112.02721
Autor:
Tan, Fiona Anting, Ng, See-Kiong
Publikováno v:
FNP2021
Automatic identification of cause-effect spans in financial documents is important for causality modelling and understanding reasons that lead to financial events. To exploit the observation that words are more connected to other words with the same
Externí odkaz:
http://arxiv.org/abs/2110.02991