Papers with minimum risk training
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (C18-1)
Copied to clipboard
| Challenge: | Morphological string transduction involves mapping one word form into another, possibly given a feature specification for the mapping. |
| Approach: | They propose a neural transition-based model that uses a simple set of edit actions for morphological transduction tasks such as reinflection and reinflation. |
| Outcome: | The proposed model outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
Copied to clipboard
| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
Copied to clipboard
Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
Copied to clipboard
| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains. |
| Approach: | They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on 21 datasets. |