Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Oraby, Shereen"'
Autor:
Ferraz, Thomas Palmeira, Mehta, Kartik, Lin, Yu-Hsiang, Chang, Haw-Shiuan, Oraby, Shereen, Liu, Sijia, Subramanian, Vivek, Chung, Tagyoung, Bansal, Mohit, Peng, Nanyun
Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to create a social media post "in a funny tone" with "no hashtag"). Desp
Externí odkaz:
http://arxiv.org/abs/2410.06458
Autor:
Tian, Yufei, Narayan-Chen, Anjali, Oraby, Shereen, Cervone, Alessandra, Sigurdsson, Gunnar, Tao, Chenyang, Zhao, Wenbo, Chen, Yiwen, Chung, Tagyoung, Huang, Jing, Peng, Nanyun
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than unconstrained lyric generation as the music imposes additional constraints
Externí odkaz:
http://arxiv.org/abs/2305.19228
Autor:
Tian, Yufei, Narayan-Chen, Anjali, Oraby, Shereen, Cervone, Alessandra, Sigurdsson, Gunnar, Tao, Chenyang, Zhao, Wenbo, Chung, Tagyoung, Huang, Jing, Peng, Nanyun
Automatic song writing is a topic of significant practical interest. However, its research is largely hindered by the lack of training data due to copyright concerns and challenged by its creative nature. Most noticeably, prior works often fall short
Externí odkaz:
http://arxiv.org/abs/2305.07760
Autor:
Sun, Jiao, Narayan-Chen, Anjali, Oraby, Shereen, Gao, Shuyang, Chung, Tagyoung, Huang, Jing, Liu, Yang, Peng, Nanyun
Previous work on pun generation commonly begins with a given pun word (a pair of homophones for heterographic pun generation and a polyseme for homographic pun generation) and seeks to generate an appropriate pun. While this may enable efficient pun
Externí odkaz:
http://arxiv.org/abs/2210.13522
Autor:
Sun, Jiao, Narayan-Chen, Anjali, Oraby, Shereen, Cervone, Alessandra, Chung, Tagyoung, Huang, Jing, Liu, Yang, Peng, Nanyun
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret le
Externí odkaz:
http://arxiv.org/abs/2210.13513
Autor:
Tsai, Alicia Y., Oraby, Shereen, Perera, Vittorio, Kao, Jiun-Yu, Du, Yuheng, Narayan-Chen, Anjali, Chung, Tagyoung, Hakkani-Tur, Dilek
Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously
Externí odkaz:
http://arxiv.org/abs/2109.12211
Natural language generators (NLGs) for task-oriented dialogue typically take a meaning representation (MR) as input. They are trained end-to-end with a corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue acts and domain attri
Externí odkaz:
http://arxiv.org/abs/2010.00150
Autor:
Du, Yuheng, Oraby, Shereen, Perera, Vittorio, Shen, Minmin, Narayan-Chen, Anjali, Chung, Tagyoung, Venkatesh, Anu, Hakkani-Tur, Dilek
Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that accurately realizes an input meaning representation. To facilitate th
Externí odkaz:
http://arxiv.org/abs/2005.05480
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant. While earlier work focused solely on the
Externí odkaz:
http://arxiv.org/abs/1907.09527
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomi
Externí odkaz:
http://arxiv.org/abs/1906.01334