Search Results for author: Mandy Guo

Found 15 papers, 4 papers with code

MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering Models

1 code implementation EACL (AdaptNLP) 2021 Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant

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.

Information Retrieval Question Answering +1

LongT5: Efficient Text-To-Text Transformer for Long Sequences

1 code implementation15 Dec 2021 Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang

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.

Abstractive Text Summarization Long-range modeling +2

Towards Universality in Multilingual Text Rewriting

no code implementations30 Jul 2021 Xavier Garcia, Noah Constant, Mandy Guo, Orhan Firat

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.

Translation

TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling

no code implementations ACL 2021 Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh

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.

Style Transfer Text Style Transfer

Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation

no code implementations ACL 2021 Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer

Independently computing embeddings for questions and answers results in late fusion of information related to matching questions to their answers.

Data Augmentation Question Answering

MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models

1 code implementation5 May 2020 Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant

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.

Information Retrieval Question Answering

Bridging the Gap for Tokenizer-Free Language Models

no code implementations27 Aug 2019 Dokook Choe, Rami Al-Rfou, Mandy Guo, Heeyoung Lee, Noah Constant

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.

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