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Found 12 papers, 3 papers with code
Exploring the Boundaries of Low-Resource BERT Distillation
We also show that the distillation of large pre-trained models is more effective in real-life scenarios where limited amounts of labeled training are available.
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction.
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text.
Efficient Few-Shot Learning Without Prompts
This simple framework requires no prompts or verbalizers, and achieves high accuracy with orders of magnitude less parameters than existing techniques.
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
In order to reduce this computational load in inference time, we present TangoBERT, a cascaded model architecture in which instances are first processed by an efficient but less accurate first tier model, and only part of those instances are additionally processed by a less efficient but more accurate second tier model.
InterpreT: An Interactive Visualization Tool for Interpreting Transformers
With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction
A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction.
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction.
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion.
Term Set Expansion based NLP Architect by Intel AI Lab
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class.
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
