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Found 7 papers, 1 papers with code
Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes
However, such NLG models suffer from two drawbacks: (1) some of the previous session queries could be noisy and irrelevant to the user intent for the current prefix, and (2) NLG models cannot directly incorporate historical query popularity.
Compression of Deep Learning Models for Text: A Survey
In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanksto deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)networks, and Transformer [120] based models like Bidirectional Encoder Representations from Transformers (BERT) [24], GenerativePre-training Transformer (GPT-2) [94], Multi-task Deep Neural Network (MT-DNN) [73], Extra-Long Network (XLNet) [134], Text-to-text transfer transformer (T5) [95], T-NLG [98] and GShard [63].
Insights from Building an Open-Ended Conversational Agent
We conceptualized one such conversational agent, Microsoft{'}s {``}Ruuh{''} with the promise to be able to talk to its users on any subject they choose.
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text
However, the context of dialogue can prove helpful in detection of the emotion.
Using AI for Economic Upliftment of Handicraft Industry
For this age-old industry to survive the global competition, it is imperative to integrate contemporary designs with Indian handicrafts.
A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model.
Emulating Human Conversations using Convolutional Neural Network-based IR
Conversational agents ("bots") are beginning to be widely used in conversational interfaces.
