David C. Uthus

Also published as: David Uthus


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RISE: Leveraging Retrieval Techniques for Summarization Evaluation
David Uthus | Jianmo Ni
Findings of the Association for Computational Linguistics: ACL 2023

Evaluating automatically-generated text summaries is a challenging task. While there have been many interesting approaches, they still fall short of human evaluations. We present RISE, a new approach for evaluating summaries by leveraging techniques from information retrieval. 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. RISE is especially well suited when working on new datasets where one may not have reference summaries available for evaluation. We conduct comprehensive experiments on the SummEval benchmark (Fabbri et al., 2021) and a long document summarization benchmark. The results show that RISE consistently achieves higher correlation with human evaluations compared to many past approaches to summarization evaluation. Furthermore, RISE also demonstrates data-efficiency and generalizability across languages.

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mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
David Uthus | Santiago Ontanon | Joshua Ainslie | Mandy Guo
Findings of the Association for Computational Linguistics: EMNLP 2023

We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architecture of LongT5, while leveraging the multilingual datasets used for pretraining mT5 and the pretraining tasks of UL2. We evaluate this model on a variety of multilingual summarization and question-answering tasks, and the results show stronger performance for mLongT5 when compared to existing multilingual models such as mBART or M-BERT.

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CoLT5: Faster Long-Range Transformers with Conditional Computation
Joshua Ainslie | Tao Lei | Michiel de Jong | Santiago Ontanon | Siddhartha Brahma | Yury Zemlyanskiy | David Uthus | Mandy Guo | James Lee-Thorp | Yi Tay | Yun-Hsuan Sung | Sumit Sanghai
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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. However, not all tokens are equally important, especially for longer documents. We propose CoLT5, a long-input Transformer model that builds on this intuition by employing conditional computation, devoting more resources to important tokens in both feedforward and attention layers. We show that CoLT5 achieves stronger performance than LongT5 with much faster training and inference, achieving SOTA on the long-input SCROLLS benchmark. Moreover, CoLT5 can effectively and tractably make use of extremely long inputs, showing strong gains up to 64k input length.


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LongT5: Efficient Text-To-Text Transformer for Long Sequences
Mandy Guo | Joshua Ainslie | David Uthus | Santiago Ontanon | Jianmo Ni | Yun-Hsuan Sung | Yinfei Yang
Findings of the Association for Computational Linguistics: NAACL 2022

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. In this paper, we present LongT5, a new model that explores the effects of scaling both the input length and model size at the same time. Specifically, we integrate attention ideas from long-input transformers (ETC), and adopt pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call Transient Global (TGlobal), which mimics ETC’s local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization and question answering tasks, as well as outperform the original T5 models on these tasks. We have open sourced our architecture and training code, as well as our pre-trained model checkpoints.

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Augmenting Poetry Composition with Verse by Verse
David Uthus | Maria Voitovich | R.J. Mical
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

We describe Verse by Verse, our experiment in augmenting the creative process of writing poetry with an AI. We have created a group of AI poets, styled after various American classic poets, that are able to offer as suggestions generated lines of verse while a user is composing a poem. In this paper, we describe the underlying system to offer these suggestions. This includes a generative model, which is tasked with generating a large corpus of lines of verse offline and which are then stored in an index, and a dual-encoder model that is tasked with recommending the next possible set of verses from our index given the previous line of verse.


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TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
Parker Riley | Noah Constant | Mandy Guo | Girish Kumar | David Uthus | Zarana Parekh
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a novel approach to the problem of text style transfer. 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. We adapt T5 (Raffel et al., 2020), a strong pretrained text-to-text model, to extract a style vector from text and use it to condition the decoder to perform style transfer. As our label-free training results in a style vector space encoding many facets of style, we recast transfers as “targeted restyling” vector operations that adjust specific attributes of the input while preserving others. We demonstrate that training on unlabeled Amazon reviews data results in a model that is competitive on sentiment transfer, even compared to models trained fully on labeled data. Furthermore, applying our novel method to a diverse corpus of unlabeled web text results in a single model capable of transferring along multiple dimensions of style (dialect, emotiveness, formality, politeness, sentiment) despite no additional training and using only a handful of exemplars at inference time.


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Investigating Societal Biases in a Poetry Composition System
Emily Sheng | David Uthus
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

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. Creative language applications are meant for direct interaction with users, so it is important to quantify and mitigate societal biases in these applications. We introduce a novel study on a pipeline to mitigate societal biases when retrieving next verse suggestions in a poetry composition system. Our results suggest that data augmentation through sentiment style transfer has potential for mitigating societal biases.


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Detecting Bot-Answerable Questions in Ubuntu Chat
David Uthus | David Aha
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Plans Toward Automated Chat Summarization
David C. Uthus | David W. Aha
Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages