Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight

By Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, Dawei Yin. 2020

73 citations recorded by the source corpus.

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear. This impedes the learning of those data-driven neural dialogue models. Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples. In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously. In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data. Note that, the proposed data manipulation framework is fully data-driven and learnable. It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples. Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Dialogue & Conversational AI, Natural Language Generation

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, neutral): “, 2020b,c), and automated text augmentation (Hu et al., 2019; Cai et al., 2020).”
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020, unknown): “…create augmented instances by manipulating a few words in the original instance, such as word replacement (Zhang et al., 2015; Wang and Yang, 2015; Cai et al., 2020), random deletion (Wei and Zou, 2019), or word position swap (S¸ahin and Steedman, 2018; Min et al., 2020); or create entirely…”
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021, methodology): “Paraphrasing has been widely adopted as a data augmentation technique in various NLP tasks (Yu et al., 2018; Xie et al., 2019; Kumar et al., 2019; He et al., 2019; Chen et al., 2020b,c; Cai et al., 2020), as it generally provides more diverse augmented text with different word choices and sentence s”
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" (2023, methodology): “Similar approaches are used in the model augmentations literature (Sennrich et al., 2016; Cai et al., 2020; Kobayashi, 2018; Eldan & Li, 2023).”