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Found 18 papers, 1 papers with code
BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster
In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no, moderate, or severe depression.
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition
Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER.
HateDebias: On the Diversity and Variability of Hate Speech Debiasing
To further meet the variability (i. e., the changing of bias attributes in datasets), we reorganize datasets to follow the continuous learning setting.
An interpretability framework for Similar case matching
Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity.
A BERT-based Unsupervised Grammatical Error Correction Framework
Grammatical error correction (GEC) is a challenging task of natural language processing techniques.
Model and Evaluation: Towards Fairness in Multilingual Text Classification
The multilingual text representation module uses a multilingual pre-trained language model to represent the text, the language fusion module makes the semantic spaces of different languages tend to be consistent through contrastive learning, and the text debiasing module uses contrastive learning to make the model unable to identify sensitive attributes' information.
How to choose "Good" Samples for Text Data Augmentation
In this paper, we propose a novel self-training selection framework with two selectors to select the high-quality samples from data augmentation.
An Effective Deployment of Contrastive Learning in Multi-label Text Classification
We explore the effectiveness of contrastive learning for multi-label text classification tasks by the employment of these novel losses and provide a set of baseline models for deploying contrastive learning techniques on specific tasks.
A Chinese Spelling Check Framework Based on Reverse Contrastive Learning
Inspired by contrastive learning, we present a novel framework for Chinese spelling checking, which consists of three modules: language representation, spelling check and reverse contrastive learning.
An Analysis of the Differences Among Regional Varieties of Chinese in Malay Archipelago
Chinese features prominently in the Chinese communities located in the nations of Malay Archipelago.
A New Evaluation Method: Evaluation Data and Metrics for Chinese Grammar Error Correction
In terms of the reference-based metric, we introduce sentence-level accuracy and char-level BLEU to evaluate the corrected sentences.
Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages
The Yunshan Cup 2020 track focused on creating a framework for evaluating different methods of part-of-speech (POS).
A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition
In this paper, we describe our novel dual-contrastive framework ConCNER for cross-lingual NER under the scenario of limited source-language labeled data.
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
In the distillation XABSA task, we further explore the comparative effectiveness of different data (source dataset, translated dataset, and code-switched dataset).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Multilingual Text Classification for Dravidian Languages
On the other hand, in view of the problem that the model cannot well recognize and utilize the correlation among languages, we further proposed a language-specific representation module to enrich semantic information for the model.
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network
However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness.
LaoPLM: Pre-trained Language Models for Lao
In this work, we construct a text classification dataset to alleviate the resource-scare situation of the Lao language.
An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition
In this work, we propose a dataset construction framework, which is based on labeled datasets of homologous languages and iterative optimization, to build a Malay NER dataset (MYNER) comprising 28, 991 sentences (over 384 thousand tokens).
