Goal-Oriented Dialog
24 papers with code • 1 benchmarks • 6 datasets
Achieving a pre-defined goal through a dialog.
Doc2Dial
DSTC7 Task 1
Latest papers
An Annotated Corpus of Reference Resolution for Interpreting Common Grounding
Common grounding is the process of creating, repairing and updating mutual understandings, which is a fundamental aspect of natural language conversation.
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn.
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context
Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.
Sequential Attention-based Network for Noetic End-to-End Response Selection
alibaba/esim-response-selection
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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.
Efficient Dialog Policy Learning via Positive Memory Retention
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting.
NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager
sld/convai-bot-1337
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COLING 2018
We present bot{\#}1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI).
Zero-Shot Dialog Generation with Cross-Domain Latent Actions
snakeztc/NeuralDialog-ZSDG
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WS 2018
This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data.
NE-Table: A Neural key-value table for Named Entities
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
