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Found 11 papers, 4 papers with code
Reconsidering Sentence-Level Sign Language Translation
Then as a case study, we perform the first human baseline for sign language translation that actually substitutes a human into the machine learning task framing, rather than provide the human with the entire document as context.
Memory Augmented Language Models through Mixture of Word Experts
We demonstrate that MoWE performs significantly better than the T5 family of models with similar number of FLOPs in a variety of NLP tasks.
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.
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
RISE: Leveraging Retrieval Techniques for Summarization Evaluation
RISE is first trained as a retrieval task using a dual-encoder retrieval setup, and can then be subsequently utilized for evaluating a generated summary given an input document, without gold reference summaries.
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
Augmenting Poetry Composition with Verse by Verse
We describe Verse by Verse, our experiment in augmenting the creative process of writing poetry with an AI.
Investigating Societal Biases in a Poetry Composition System
There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks remains underexplored.
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.
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.
