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Found 27 papers, 9 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.
CLASS: Enhancing Cross-Modal Text-Molecule Retrieval Performance and Training Efficiency
Cross-modal text-molecule retrieval task bridges molecule structures and natural language descriptions.
Ranked #1 on
Cross-Modal Retrieval
on ChEBI-20
A Simple Yet Effective Corpus Construction Framework for Indonesian Grammatical Error Correction
How to efficiently construct high-quality evaluation corpora for GEC in low-resource languages has become a significant challenge.
Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning
Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive.
$\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning
In this work, we propose a novel framework for alpha discovery using DRL by formulating the alpha discovery process as program construction.
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.
A BERT-based Unsupervised Grammatical Error Correction Framework
Grammatical error correction (GEC) is a challenging task of natural language processing techniques.
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
In this paper, we propose a novel active imitation learning framework based on a teacher-student interaction model, in which the teacher's goal is to identify the best teaching behavior and actively affect the student's learning process.
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.
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.
Subgraph Frequency Distribution Estimation using Graph Neural Networks
Small subgraphs (graphlets) are important features to describe fundamental units of a large network.
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning
Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting.
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).
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)
+2
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.
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.
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning
Experiments on MuJoCo and Hand Manipulation Suite tasks show that the agents deployed with our method achieve similar performance as it has in the source domain, while those deployed with previous methods designed for same-modal domain adaptation suffer a larger performance gap.
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).
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
To tackle the issue of limited action execution in RL, this paper first formalizes the problem as a Sparse Action Markov Decision Process (SA-MDP), in which specific actions in the action space can only be executed for a limited time.
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning
In reinforcement learning, experience replay stores past samples for further reuse.
Cross-Modal Domain Adaptation for Reinforcement Learning
Domain adaptation is a promising direction for deploying RL agents in real-world applications, where vision-based robotics tasks constitute an important part.
Offline Imitation Learning with a Misspecified Simulator
In this work, we investigate policy learning in the condition of a few expert demonstrations and a simulator with misspecified dynamics.
FGN: Fusion Glyph Network for Chinese Named Entity Recognition
Except for adding glyph information, this method may also add extra interactive information with the fusion mechanism.
Ranked #2 on
Chinese Named Entity Recognition
on Resume NER
Chinese Named Entity Recognition
named-entity-recognition
+3
