Conversational Response Selection
30 papers with code • 13 benchmarks • 10 datasets
Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information.