Daphne Ippolito


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Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
Daphne Ippolito | Liunian Harold Li | Maria Leonor Pacheco | Danqi Chen | Nianwen Xue
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop

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Deduplicating Training Data Makes Language Models Better
Katherine Lee | Daphne Ippolito | Andrew Nystrom | Chiyuan Zhang | Douglas Eck | Chris Callison-Burch | Nicholas Carlini
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings.As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data.We develop two tools that allow us to deduplicate training datasets—for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times.Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer training steps to achieve the same or better accuracy.We can also reduce train-test overlap, which affects over 4% of the validation set of standard datasets, thus allowing for more accurate evaluation.Code for deduplication is released at https://github.com/google-research/deduplicate-text-datasets.

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A Recipe for Arbitrary Text Style Transfer with Large Language Models
Emily Reif | Daphne Ippolito | Ann Yuan | Andy Coenen | Chris Callison-Burch | Jason Wei
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper, we leverage large language models (LLMs) to perform zero-shot text style transfer. We present a prompting method that we call augmented zero-shot learning, which frames style transfer as a sentence rewriting task and requires only a natural language instruction, without model fine-tuning or exemplars in the target style. Augmented zero-shot learning is simple and demonstrates promising results not just on standard style transfer tasks such as sentiment, but also on arbitrary transformations such as ‘make this melodramatic’ or ‘insert a metaphor.’

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The Case for a Single Model that can Both Generate Continuations and Fill-in-the-Blank
Daphne Ippolito | Liam Dugan | Emily Reif | Ann Yuan | Andy Coenen | Chris Callison-Burch
Findings of the Association for Computational Linguistics: NAACL 2022

The task of inserting text into a specified position in a passage, known as fill in the blank (FitB), is useful for a variety of applications where writers interact with a natural language generation (NLG) system to craft text. While previous work has tackled this problem with models trained specifically to do fill in the blank, a more useful model is one that can effectively perform _both_ FitB and continuation tasks. In this work, we evaluate the feasibility of using a single model to do both tasks. We show that models pre-trained with a FitB-style objective are capable of both tasks, while models pre-trained for continuation are not. Finally, we show how these models can be easily finetuned to allow for fine-grained control over the length and word choice of the generation.


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Trading Off Diversity and Quality in Natural Language Generation
Hugh Zhang | Daniel Duckworth | Daphne Ippolito | Arvind Neelakantan
Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)

For open-ended language generation tasks such as storytelling or dialogue, choosing the right decoding algorithm is vital for controlling the tradeoff between generation quality and diversity. However, there presently exists no consensus on which decoding procedure is best or even the criteria by which to compare them. In this paper, we cast decoding as a tradeoff between response quality and diversity, and we perform the first large-scale evaluation of decoding methods along the entire quality-diversity spectrum. Our experiments confirm the existence of the likelihood trap: the counter-intuitive observation that high likelihood sequences are often surprisingly low quality. We also find that when diversity is a priority, all methods perform similarly, but when quality is viewed as more important, nucleus sampling (Holtzman et al., 2019) outperforms all other evaluated decoding algorithms.


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RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
Liam Dugan | Daphne Ippolito | Arun Kirubarajan | Chris Callison-Burch
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.

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Automatic Detection of Generated Text is Easiest when Humans are Fooled
Daphne Ippolito | Daniel Duckworth | Chris Callison-Burch | Douglas Eck
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent advancements in neural language modelling make it possible to rapidly generate vast amounts of human-sounding text. The capabilities of humans and automatic discriminators to detect machine-generated text have been a large source of research interest, but humans and machines rely on different cues to make their decisions. Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies—top-_k_, nucleus sampling, and untruncated random sampling—and show that improvements in decoding methods have primarily optimized for fooling humans. This comes at the expense of introducing statistical abnormalities that make detection easy for automatic systems. We also show that though both human and automatic detector performance improve with longer excerpt length, even multi-sentence excerpts can fool expert human raters over 30% of the time. Our findings reveal the importance of using both human and automatic detectors to assess the humanness of text generation systems.

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Toward Better Storylines with Sentence-Level Language Models
Daphne Ippolito | David Grangier | Douglas Eck | Chris Callison-Burch
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentence-level language model can focus on longer range dependencies, which are crucial for multi-sentence coherence. Rather than dealing with individual words, our method treats the story so far as a list of pre-trained sentence embeddings and predicts an embedding for the next sentence, which is more efficient than predicting word embeddings. Notably this allows us to consider a large number of candidates for the next sentence during training. We demonstrate the effectiveness of our approach with state-of-the-art accuracy on the unsupervised Story Cloze task and with promising results on larger-scale next sentence prediction tasks.


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ChatEval: A Tool for Chatbot Evaluation
João Sedoc | Daphne Ippolito | Arun Kirubarajan | Jai Thirani | Lyle Ungar | Chris Callison-Burch
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Open-domain dialog systems (i.e. chatbots) are difficult to evaluate. The current best practice for analyzing and comparing these dialog systems is the use of human judgments. However, the lack of standardization in evaluation procedures, and the fact that model parameters and code are rarely published hinder systematic human evaluation experiments. We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems. Researchers can submit their trained models to the ChatEval web interface and obtain comparisons with baselines and prior work. The evaluation code is open-source to ensure standardization and transparency. In addition, we introduce open-source baseline models and evaluation datasets. ChatEval can be found at https://chateval.org.

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Unsupervised Hierarchical Story Infilling
Daphne Ippolito | David Grangier | Chris Callison-Burch | Douglas Eck
Proceedings of the First Workshop on Narrative Understanding

Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.

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Comparison of Diverse Decoding Methods from Conditional Language Models
Daphne Ippolito | Reno Kriz | João Sedoc | Maria Kustikova | Chris Callison-Burch
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

While conditional language models have greatly improved in their ability to output high quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that rerank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. In this work, we perform an extensive survey of decoding-time strategies for generating diverse outputs from a conditional language model. In addition, we present a novel method where we over-sample candidates, then use clustering to remove similar sequences, thus achieving high diversity without sacrificing quality.


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Learning Translations via Images with a Massively Multilingual Image Dataset
John Hewitt | Daphne Ippolito | Brendan Callahan | Reno Kriz | Derry Tanti Wijaya | Chris Callison-Burch
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. %We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at http://multilingual-images.org/.

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ChatEval: A Tool for the Systematic Evaluation of Chatbots
João Sedoc | Daphne Ippolito | Arun Kirubarajan | Jai Thirani | Lyle Ungar | Chris Callison-Burch
Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)