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Found 16 papers, 5 papers with code
A Repository of Conversational Datasets
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
BIG-bench Machine Learning
Conversational Response Selection
+1
ConveRT: Efficient and Accurate Conversational Representations from Transformers
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.
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Conversational Response Selection
on PolyAI Reddit
Efficient Intent Detection with Dual Sentence Encoders
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task.
Training Neural Response Selection for Task-Oriented Dialogue Systems
Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks.
Question-Answer Selection in User to User Marketplace Conversations
Sellers in user to user marketplaces can be inundated with questions from potential buyers.
Efficient Natural Language Response Suggestion for Smart Reply
This paper presents a computationally efficient machine-learned method for natural language response suggestion.
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
We present PolyResponse, a conversational search engine that supports task-oriented dialogue.
ConVEx: Data-Efficient and Few-Shot Slot Labeling
We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks.
Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns
Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications.
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e. g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation.
