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Found 10 papers, 7 papers with code
DATAMAFIA at WNUT-2020 Task 2: A Study of Pre-trained Language Models along with Regularization Techniques for Downstream Tasks
This document describes the system description developed by team datamafia at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets.
Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling
Experimental results show that our gated architecture with pre-trained language models perform significantly better that the non-gated counterparts and other state-of-the-art error correction models in correcting spelling and grammatical errors.
Manifold-Preserving Transformers are Effective for Short-Long Range Encoding
In this work, we propose TransJect, an encoder model that guarantees a theoretical bound for layer-wise distance preservation between a pair of tokens.
Persona-aware Generative Model for Code-mixed Language
We propose a Persona-aware Generative Model for Code-mixed Generation, PARADOX, a novel Transformer-based encoder-decoder model that encodes an utterance conditioned on a user's persona and generates code-mixed texts without monolingual reference data.
A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer
Learning the semantics and morphology of code-mixed language remains a key challenge, due to scarcity of data and unavailability of robust and language-invariant representation learning technique.
HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation
In this paper, we propose HIT, a robust representation learning method for code-mixed texts.
An Embedding-based Joint Sentiment-Topic Model for Short Texts
Many companies need to extract meaningful information (which may include thematic content as well as semantic polarity) out of such short texts to understand users' behaviour.
An Autonomous Negotiating Agent Framework with Reinforcement Learning Based Strategies and Adaptive Strategy Switching Mechanism
Another problem with most negotiation strategies is their incapability of adapting to dynamic variation of the opponent's behaviour within a single negotiation session resulting in poor performance.
Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM
With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc.
