Search Results for author:
Found 24 papers, 17 papers with code
Language Is Not All You Need: Aligning Perception with Language Models
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.
Ranked #2 on
Image Captioning
on Flickr30k Captions test
(CIDEr metric)
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension
Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes.
Optimizing Prompts for Text-to-Image Generation
Instead of laborious human engineering, we propose prompt adaptation, a general framework that automatically adapts original user input to model-preferred prompts.
TorchScale: Transformers at Scale
Large Transformers have achieved state-of-the-art performance across many tasks.
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning
In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more parameter efficient, thereby promoting better adoption in resource-constrained scenarios and practical applications.
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Unsupervised Question Answering via Answer Diversifying
Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models.
Language Models are General-Purpose Interfaces
Experimental results across various language-only and vision-language benchmarks show that our model outperforms or is competitive with specialized models on finetuning, zero-shot generalization, and few-shot learning.
Ranked #2 on
Image Captioning
on nocaps val
On the Representation Collapse of Sparse Mixture of Experts
We also present a comprehensive analysis on the representation and routing behaviors of our models.
Cross-Lingual Phrase Retrieval
In this paper, we propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%.
Cross-Lingual Language Model Meta-Pretraining
The success of pretrained cross-lingual language models relies on two essential abilities, i. e., generalization ability for learning downstream tasks in a source language, and cross-lingual transferability for transferring the task knowledge to other languages.
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training.
Ranked #1 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Consistency Regularization for Cross-Lingual Fine-Tuning
Fine-tuning pre-trained cross-lingual language models can transfer task-specific supervision from one language to the others.
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
The cross-lingual language models are typically pretrained with masked language modeling on multilingual text or parallel sentences.
MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs
Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks.
A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data.
Ranked #3 on
Named Entity Recognition (NER)
on SciERC
(using extra training data)
Low Resource Named Entity Recognition
named-entity-recognition
+2
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders
Multilingual machine translation enables a single model to translate between different languages.
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training
In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts.
Ranked #16 on
Zero-Shot Cross-Lingual Transfer
on XTREME
Generating Informative Dialogue Responses with Keywords-Guided Networks
Specifically, KW-Seq2Seq first uses a keywords decoder to predict some topic keywords, and then generates the final response under the guidance of them.
Can Monolingual Pretrained Models Help Cross-Lingual Classification?
Multilingual pretrained language models (such as multilingual BERT) have achieved impressive results for cross-lingual transfer.
Cross-Lingual Natural Language Generation via Pre-Training
In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages.
Complicated Table Structure Recognition
It also attracts lots of attention to recognize the table structures in PDF files.
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.
