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Found 12 papers, 7 papers with code
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary.
Understanding Iterative Revision from Human-Written Text
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process.
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision
Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants.
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
The proposed stochastic function is sampled from a Gaussian process prior to (1) provide infinite number of joint Gaussian distributions of random context variables (diversity-promoting) and (2) explicitly model dependency between context variables (accurate-encoding).
SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
Transformer-based pre-trained language models boost the performance of open-domain dialogue systems.
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation
In task-oriented dialogue systems, response generation from meaning representations (MRs) often suffers from limited training examples, due to the high cost of annotating MR-to-Text pairs.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
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Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Contextualizing Variation in Text Style Transfer Datasets
Text style transfer involves rewriting the content of a source sentence in a target style.
Explaining Predictive Uncertainty by Looking Back at Model Explanations
Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and gaining their trust on model predictions, while has been largely ignored in prior works.
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows
Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it.
Blending Reward Functions via Few Expert Demonstrations for Faithful and Accurate Knowledge-Grounded Dialogue Generation
The development of trustworthy conversational information-seeking systems relies on dialogue models that can generate faithful and accurate responses based on relevant knowledge texts.
