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
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
observeai-research/nli-fsl
•
•
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.
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.
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
bruzwen/ddxplus
•
•
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.
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.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
salesforce/DNNC-few-shot-intent
•
•
EMNLP 2020
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.
Effects of Naturalistic Variation in Goal-Oriented Dialog
Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
amazon-science/multiatis
•
•
EMNLP 2020
We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.
ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services.
Sequential Neural Networks for Noetic End-to-End Response Selection
alibaba/esim-response-selection
•
•
The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (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.
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input.
