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Found 8 papers, 0 papers with code
Learning-To-Embed: Adopting Transformer based models for E-commerce Products Representation Learning
For both the tasks, we collect an evaluation data from the fashion e-commerce platform and observe that XLNET model outperform other variants with a MRR of 0. 5 for NPR and NDCG of 0. 634 for CR.
Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning
User activities in a session can be classified into two groups: Known Intent and Unknown intent.
ListBERT: Learning to Rank E-commerce products with Listwise BERT
Our experiments indicate that the RoBERTa model fine-tuned with an NDCG based surrogate loss function(approxNDCG) achieves an NDCG improvement of 13. 9% compared to other popular listwise loss functions like ListNET and ListMLE.
Neural Search: Learning Query and Product Representations in Fashion E-commerce
For the product retrieval task, RoBERTa model is able to outperform other two models with an improvement of 164. 7% in Precision@50 and 145. 3% in Recall@50.
Deep Contextual Embeddings for Address Classification in E-commerce
Once pre-trained, the RoBERTa model can be fine-tuned for various downstream tasks in supply chain like pincode suggestion and geo-coding.
``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text
Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier.
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text
We analyze the challenges of the problem, and present Rule-based, Machine Learning and Deep Learning approaches to detect sarcasm in numerical portions of text.
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings
Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis.
