Белгіленген деректер

The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, …...

We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combina- tion of labeled and unlabeled …...

Obtaining labeled data is a significant obstacle for many NLP tasks. Recently, online games have been proposed as a new way of obtain- ing labeled data; games attract users by …...

A major opportunity for NLP to have a real- world impact is in helping educators score stu- dent writing, particularly content-based writ- ing (i.e., the task of automated short answer …...

In this work, we propose a semi- supervised method for short text clus- tering, where we represent texts as dis- tributed vectors with neural networks, and use a small amount …...

AbstractPre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level …...

We tested the viability of partnering with local developers to create custom annotation applications and to recruit and motivate crowd contributors from their communities to perform an annotation task consisting …...

AbstractDue to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both …...

AbstractUser modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit …...

AbstractWe tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA …...

AbstractNeural methods have been shown to achieve high performance in Named Entity Recognition (NER), but rely on costly high-quality labeled data for training, which is not always available across languages. …...

AbstractThe great majority of languages in the world are considered under-resourced for successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in …...

AbstractFor text-level discourse analysis there are various discourse schemes but relatively few labeleddata because discourse research is still immature and it is labor-intensive to annotate the innerlogic of a text. …...

AbstractThe speaker-follower models have proven to be effective in vision-and-language navigation, where a speaker model is used to synthesize new instructions to augment the training data for a follower navigation …...

AbstractTraining language models to learn from human instructions for zero-shot cross-task generalization has attracted much attention in NLP communities. Recently, instruction tuning (IT), which fine-tunes a pre-trained language model on …...

AbstractA critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain. Although a small amount of labeled data …...

AbstractNoise Learning is important in the task of text classification which depends on massive labeled data that could be error-prone. However, we find that noise learning in text classification is …...

AbstractLabelled data is the foundation of most natural language processing tasks. However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should …...

AbstractDiscovering new intents is of great significance for establishing the Task-Oriented Dialogue System. Most existing methods either cannot transfer prior knowledge contained in known intents or fall into the dilemma …...

AbstractOur team focuses on the multimodal domain of images and texts, we propose a model that can learn the matching relationship between text-image pairs by contrastive learning. More specifically, We …...