A novel training objective for text generation
a unified framework for LFLL based on prompt tuning of T5
We show empirically that increasing the density of negative samples improves the basic model, and using a global negative queue further improves and stabilizes the model while training with hard negative samples.
A unified speaker adaptation approach consisting of feature adaptation and model adaptation for ASR.
A data augmentation method for NER.
A novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework.
We propose UXLA, a novel data augmentation framework for self-supervised learning in zero-resource transfer learning scenarios.
Coherence models are typically evaluated only on synthetic tasks, which may not be representative of their performance in downstream applications. To investigate how representative the synthetic tasks are of downstream use cases, we conduct experiments on benchmarking well-known traditional and neural coherence models on synthetic sentence ordering tasks, and contrast this with their performance on three downstream applications: coherence evaluation for MT and summarization, and next utterance prediction in retrieval-based dialog.
Data augmentation for low resource tagging.
An extensive, targeted dataset that can be used as a test suite for pronoun translation, covering multiple source languages and different pronoun errors drawn from real system translations, for English
A unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework.
A hierarchical pointer network parsers applied to dependency and sentence-level discourse parsing tasks.
Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many NLP applications.
A demo of malay english Machine translation system.
Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many text mining applications.
A neural approach for modeling coherence of asynchronus conversation
This search tool helps you to find good answers to your question by searching through previously asked questions in the Qatarliving forum.
Python implementation of a number of deep neural networks classifiers for the classification of crisis-related data on Twitter.
This paper shows with a semi-supervised algorithm that BLI is more suitable through Non-Linear Mapping (specially for low resource languages).
This resource addresses the problem of speech act recognition in written asynchronous conversations
This parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intra-sentential parsing and the other for multi-sentential parsing.
Fully unsupervised mining method that can built synthetic parallel data for unsupervised machine translation.
A novel strategy to improve unsupervised MT by using back-translation with multiple models.
A simple way to boost many NMT tasks by using multiple backward and forward models.
This resource contains the source code of our ACL-2020 paper entitled Differentiable Window for Dynamic Local Attention