Search Results for author:
Found 21 papers, 12 papers with code
UniSumm: Unified Few-shot Summarization with Multi-Task Pre-Training and Prefix-Tuning
The diverse demands of different summarization tasks and their high annotation costs are driving a need for few-shot summarization.
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Z-Code++ creates new state of the art on 9 out of 13 text summarization tasks across 5 languages.
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training
Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a shared embedding space.
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks.
CLIP-Event: Connecting Text and Images with Event Structures
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text.
Leveraging Knowledge in Multilingual Commonsense Reasoning
Then we utilize a diverse of 4 English knowledge sources to provide more comprehensive coverage of knowledge in different formats.
MA-CLIP: Towards Modality-Agnostic Contrastive Language-Image Pre-training
Large-scale multimodal contrastive pretraining has demonstrated great utility to support high performance in a range of downstream tasks by mapping multiple modalities into a shared embedding space.
Does Knowledge Help General NLU? An Empirical Study
It is often observed in knowledge-centric tasks (e. g., common sense question and answering, relation classification) that the integration of external knowledge such as entity representation into language models can help provide useful information to boost the performance.
Fusing Context Into Knowledge Graph for Commonsense Question Answering
However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.
Ranked #3 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
Mixed-Lingual Pre-training for Cross-lingual Summarization
Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language.
Predicting Performance for Natural Language Processing Tasks
Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting.
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties.
Enhancing Factual Consistency of Abstractive Summarization
Automatic abstractive summaries are found to often distort or fabricate facts in the article.
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.
Formality Style Transfer with Hybrid Textual Annotations
Its challenge is the lack of large-scale sentence-aligned parallel data.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
Unsupervised Cross-lingual Transfer of Word Embedding Spaces
Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages.
Cross-lingual Distillation for Text Classification
Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available.
Leveraging Multilingual Training for Limited Resource Event Extraction
Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance.
