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Found 23 papers, 5 papers with code
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation
Low-Rank Adaptation (LoRA) introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources.
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.
A Neural Conversation Generation Model via Equivalent Shared Memory Investigation
Nevertheless, few works utilized the knowledge extracted from similar conversations for utterance generation.
Demystifying Learning of Unsupervised Neural Machine Translation
Unsupervised Neural Machine Translation or UNMT has received great attention in recent years.
Cross Copy Network for Dialogue Generation
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.
AI-lead Court Debate Case Investigation
The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial.
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.
Understanding Learning Dynamics for Neural Machine Translation
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Generalization to unseen instances is our eternal pursuit for all data-driven models.
Modeling Future Cost for Neural Machine Translation
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
Multimodal Matching Transformer for Live Commenting
In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities.
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization
Many Data Augmentation (DA) methods have been proposed for neural machine translation.
Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability
This irregularity makes the evaluation results over-estimated and affects models' generalization ability.
Understanding and Improving Hidden Representations for Neural Machine Translation
Multilayer architectures are currently the gold standard for large-scale neural machine translation.
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.
Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT
Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist.
