Search Results for author:
Found 6 papers, 2 papers with code
Generative Adversarial Training Can Improve Neural Language Models
While deep learning in the form of recurrent neural networks (RNNs) has caused a significant improvement in neural language modeling, the fact that they are extremely prone to overfitting is still a mainly unresolved issue.
$Λ$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells
The main shortcoming of DARTS is performance collapse, where the discovered architecture suffers from a pattern of declining quality during search.
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer
This paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning.
LICD: A Language-Independent Approach for Aspect Category Detection
For a given sentence, our proposed method performs ACD based on two hypotheses: First, a category should be assigned to a sentence if there is a high semantic similarity between the sentence and a set of representative words of that category.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+6
Aspect Category Detection via Topic-Attention Network
In this paper, we propose a deep neural network method based on attention mechanism to identify different aspect categories of a given review sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
Besides, most of these supervised methods require feature engineering to perform well.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
