57 datasets found

The dataset used in the paper is a conversation emotion recognition dataset, which includes two benchmark datasets: IEMOCAP and MELD.

Text-based visual question generation model that looks into the visual content, reads the scene text, and asks a relevant and meaningful natural language question.

The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics.

A chatbot designed by Colby et al. to imitate aggressive emotions.

A multilingual version of the seq2seq architecture trained on Colossal Clean Crawled Corpus.

A hybrid retrieval-augmented/grounded version of the seq2seq architecture.

A multi-turn dialogue response generator that was evaluated using automatic metrics.

A multi-turn open-domain conversational AI seq2seq model that was trained end-to-end.

The dataset used in this paper is a large-scale conversation dataset, generated by human evaluators, with a total of 20K conversations.

The DBDC4 Japanese dataset contains dialogues from three dialogue systems named DCM, DIT, and IRS, and five other dialogue systems (IRS, MMK, MRK, TRF, and ZNK) which...

The Fourth Dialogue Breakdown Detection Challenge (DBDC4) dataset contains dialogues from a dialogue system named IRIS and six other dialogue systems (anonymised as Bot001 to...

The Ubuntu Dialogue Corpus (UDC) dataset was extracted from the Ubuntu Relay Chat Channel. Although the topics in the dataset are not as diverse as in the MTC, the dataset is...

The Movie Triples Corpus (MTC) dataset was derived from the Movie-DiC dataset by Banchs (2012). Although this dataset spans a wide range of topics with few spelling mistakes,...

The Multi-Turn Conversation Response (MTCR) dataset, which is used to evaluate the effectiveness of the Thread of Thought (ThoT) strategy in handling chaotic contextual...

The Reddit Comments dataset is constructed from publicly available user comments on submissions on the Reddit website.

The dataset used for the jewelry shop conversational chatbot, containing customer queries and responses.

The third version of the socialbot Alquist, a conversational system designed to converse coherently and engagingly with humans on popular topics.

The dataset used in the paper is the Ubuntu Corpus, which consists of dialogues from the Ubuntu technical support chat.

The dataset used for context-oriented response selecting task, which is considered as a binary classification problem.

The dataset used for training the models in the Audrey chatbot.