Goal-Oriented Dialog
24 papers with code • 1 benchmarks • 6 datasets
Achieving a pre-defined goal through a dialog.
Doc2Dial
DSTC7 Task 1
Most implemented papers
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy.
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
bruzwen/ddxplus
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EMNLP (ClinicalNLP) 2020
Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis.
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
In this work, we investigate how robust IC/SL models are to noisy data.
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
observeai-research/nli-fsl
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Our method achieves state-of-the-art results on 1-shot and 5-shot intent detection task with gains ranging from 2-8\% points in F1 score on four benchmark datasets.
