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Found 37 papers, 7 papers with code
ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts
This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dimensions.
Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition
This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results.
Chinese Named Entity Recognition
named-entity-recognition
+1
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model Compression
The teacher can continuously meta-learn the student’s learning objective to adjust its parameters for maximizing the student’s performance throughout the distillation process.
Accelerating Inference for Pretrained Language Models by Unified Multi-Perspective Early Exiting
To address this limitation, a unified horizontal and vertical multi-perspective early exiting (MPEE) framework is proposed in this study to accelerate the inference of transformer-based models.
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training
The proposed SoftMCL is conducted on both the word- and sentence-level to enhance the model's ability to learn affective information.
Personalized LoRA for Human-Centered Text Understanding
Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics.
Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues
Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence.
Ranked #1 on
Dialogue Generation
on Persona-Chat
(using extra training data)
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis
Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment analysis.
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis
Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis.
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express.
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
This paper presents the IJCNLP 2017 shared task on Dimensional Sentiment Analysis for Chinese Phrases (DSAP) which seeks to identify a real-value sentiment score of Chinese single words and multi-word phrases in the both valence and arousal dimensions.
Refining Word Embeddings for Sentiment Analysis
Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks.
YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet.
Overview of NLP-TEA 2016 Shared Task for Chinese Grammatical Error Diagnosis
This paper presents the NLP-TEA 2016 shared task for Chinese grammatical error diagnosis which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language.
