Sebastian Krause
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Text Generation with Text-Editing Models
Daniil Mirylenka
Jakub Adamek
Yue Dong
Proceedings of NAACL 2022, ACL
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Text-editing models have recently become a prominent alternative to seq2seq models for monolingual natural language generation (NLG) tasks such as grammatical error correction, text simplification, and style transfer. These tasks exhibit a large amount of textual overlap between the source and target texts. Text-editing models take advantage of this trait and learn to generate the output by predicting edit operations applied to the source sequence in contrast to seq2seq models that generate the output from scratch. Text-editing models provide several benefits over seq2seq models including faster inference speed, higher sample efficiency, and better control and interpretability of the outputs. This tutorial provides a comprehensive overview of the text-edit based approaches and current state-of-the-art models, analyzing the pros and cons of different methods. We discuss challenges related to productionization and how these models can to help mitigate hallucination and bias, both pressing challenges in the field of text generation.
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We propose a new model for grammatical error correction (GEC) which builds on a very large multilingual masked language model, covering 101 languages. To adapt our model for the GEC task, we design an unsupervised, language-agnostic pretraining objective that mimics corrections typically contained in labeled data. After finetuning on gold data, we surpass the previous state-of-the-art results on the four evaluated languages (Czech, English, German and Russian). This approach shows the power of large multilingual language models. Due to these models being non-trivial to run on non-cluster infrastructure, we employ our model to clean up the labels in the popular yet noisy Lang-8 dataset. We release this dataset and hope that the community will find it useful for further advancement of GEC.
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We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task. Target texts are reconstructed from the inputs using three main edit operations: keeping a token, deleting it, and adding a phrase before the token. To predict the edit operations, we propose a novel model, which combines a BERT encoder with an autoregressive Transformer decoder. This approach is evaluated on English text on four tasks: sentence fusion, sentence splitting, abstractive summarization, and grammar correction. LaserTagger achieves new state-of-the-art results on three of these tasks, performs comparably to a set of strong seq2seq baselines with a large number of training examples, and outperforms them when the number of examples is limited. Furthermore, we show that at inference time tagging can be more than two orders of magnitude faster than comparable seq2seq models, making it more attractive for running in a live environment.
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Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from the Web. We formulate the task of discourse connective prediction and release a dataset of 2.9M sentence pairs separated by discourse connectives for this task. Then, we evaluate the hardness of the task for human raters, apply a recently proposed decomposable attention (DA) model to this task and observe that the automatic predictor has a higher F1 than human raters (32 vs. 30). Nevertheless, under specific conditions the raters still outperform the DA model, suggesting that there is headroom for future improvements.
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Redundancy Localization for the Conversationalization of Unstructured Responses
Proceedings of the SIGDIAL 2017 Conference, Association for Computational Linguistics
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Conversational agents offer users a naturallanguage interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent’s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.
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Idest: Learning a Distributed Representation for Event Patterns
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL'15), pp. 1140-1149
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This paper describes IDEST, a new method for
learning paraphrases of event patterns. It is
based on a new neural network architecture
that only relies on the weak supervision signal
that comes from the news published on the
same day and mention the same real-world entities.
It can generalize across extractions from
different dates to produce a robust paraphrase
model for event patterns that can also capture
meaningful representations for rare patterns.
We compare it with two state-of-the-art
systems and show that it can attain comparable
quality when trained on a small dataset.
Its generalization capabilities also allow it to
leverage much more data, leading to substantial
quality improvements.
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