|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
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.
Ranked #4 on Conversational Response Selection on DSTC7 Ubuntu
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
Ranked #2 on Conversational Response Selection on DSTC7 Ubuntu
In this paper, we study the task of selecting optimal response given user and system utterance history in retrieval-based multi-turn dialog systems.
We focus on multi-turn response selection in a retrieval-based dialog system.
In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task.
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.