Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Qi, Haode"'
Autor:
Qi, Haode, Qian, Cheng, Ni, Jian, Singh, Pratyush, Fazeli, Reza, Wang, Gengyu, Shu, Zhongzheng, Wayne, Eric, Bross, Juergen
In an enterprise Virtual Assistant (VA) system, intent classification is the crucial component that determines how a user input is handled based on what the user wants. The VA system is expected to be a cost-efficient SaaS service with low training a
Externí odkaz:
http://arxiv.org/abs/2408.11799
Autor:
Bhargav, G P Shrivatsa, Neelam, Sumit, Sharma, Udit, Ikbal, Shajith, Sreedhar, Dheeraj, Karanam, Hima, Joshi, Sachindra, Dhoolia, Pankaj, Garg, Dinesh, Croutwater, Kyle, Qi, Haode, Wayne, Eric, Murdock, J William
We present an approach to build Large Language Model (LLM) based slot-filling system to perform Dialogue State Tracking in conversational assistants serving across a wide variety of industry-grade applications. Key requirements of this system include
Externí odkaz:
http://arxiv.org/abs/2406.08848
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately tagging a query as out-of-domain is particularly hard in scenarios whe
Externí odkaz:
http://arxiv.org/abs/2301.06544
Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challenges in designing robust and accurate intent detection models. First,
Externí odkaz:
http://arxiv.org/abs/2012.03929
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Using parallel data, our method aligns embeddings on the word level throu
Externí odkaz:
http://arxiv.org/abs/2010.12547