Conversational Artificial Intelligence (AI) used in industry settings can be trained to closely mimic human behaviors, including lying and deception.
Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and is often difficult to reproduce previous results due to complex configurations.
To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.
Ranked #1 on Text-Image Retrieval on MSCOCO-1k
We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks.
Ranked #1 on Open-Domain Question Answering on SQuAD1.1 dev
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search.
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains.
DialPort collects user data for connected spoken dialog systems.
This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models.
This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users.
The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus.