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Found 20 papers, 8 papers with code
MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering Models
Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This dataset paper presents MultiReQA, a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets.
MURAL: Multimodal, Multitask Representations Across Languages
Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs.
WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset
Webpages have been a rich resource for language and vision-language tasks.
A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding
Webpages have been a rich, scalable resource for vision-language and language only tasks.
CoBIT: A Contrastive Bi-directional Image-Text Generation Model
The field of vision and language has witnessed a proliferation of pre-trained foundation models.
CoLT5: Faster Long-Range Transformers with Conditional Computation
Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token.
Ranked #1 on
Long-range modeling
on SCROLLS
LongT5: Efficient Text-To-Text Transformer for Long Sequences
Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models.
Ranked #1 on
Text Summarization
on BigPatent
MURAL: Multimodal, Multitask Retrieval Across Languages
Both image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
Ranked #1 on
Semantic Image-Text Similarity
on CxC
Towards Universality in Multilingual Text Rewriting
In this work, we take the first steps towards building a universal rewriter: a model capable of rewriting text in any language to exhibit a wide variety of attributes, including styles and languages, while preserving as much of the original semantics as possible.
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time.
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation
Independently computing embeddings for questions and answers results in late fusion of information related to matching questions to their answers.
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling
We present a novel approach to the challenging problem of label-free text style transfer.
MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets.
Bridging the Gap for Tokenizer-Free Language Models
Purely character-based language models (LMs) have been lagging in quality on large scale datasets, and current state-of-the-art LMs rely on word tokenization.
Multilingual Universal Sentence Encoder for Semantic Retrieval
We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures.
Hierarchical Document Encoder for Parallel Corpus Mining
We explore using multilingual document embeddings for nearest neighbor mining of parallel data.
Character-Level Language Modeling with Deeper Self-Attention
LSTMs and other RNN variants have shown strong performance on character-level language modeling.
Ranked #8 on
Language Modelling
on Hutter Prize
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings.
