With counterfactual bandit learning, models can be trained based on positive and negative feedback received for historical predictions, with no labeled data needed. Such feedback is often available in real-world dialog systems, however, the modularized architecture commonly used in large-scale systems prevents the direct application of such algorithms. In this paper, we study the feedback attribution problem that arises when using counterfactual bandit learning for multi-domain spoken language understanding. We introduce an experimental setup to simulate the problem on small-scale public datasets, propose attribution methods inspired by multi-agent reinforcement learning and evaluate them against multiple baselines. We find that while directly using overall feedback leads to disastrous performance, our proposed attribution methods can allow training competitive models from user feedback.

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Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding

Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations. Therefore, dialogue comprehension requires diverse capabilities such as paraphrasing, summarizing, and commonsense reasoning. Towards the objective of pre-training a zero-shot dialogue comprehension model, we develop a novel narrative-guided pre-training strategy that learns by narrating the key information from a dialogue input. However, the dialogue-narrative parallel corpus for such a pre-training strategy is currently unavailable. For this reason, we first construct a dialogue-narrative parallel corpus by automatically aligning movie subtitles and their synopses. We then pre-train a BART model on the data and evaluate its performance on four dialogue-based tasks that require comprehension. Experimental results show that our model not only achieves superior zero-shot performance but also exhibits stronger fine-grained dialogue comprehension capabilities. The data and code are available at https://github.com/zhaochaocs/Diana.

Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension

Paraphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphrase generation is constrained by the access to the golden labeled data pairs where both the amount and the quality of the training data pairs are restricted. In this paper, we propose a new weakly supervised paraphrase generation approach that extends the success of a recent work that leverages reinforcement learning for effective model training with data selection. While data selection is privileged for the target task which has noisy data, developing a reinforced selective learning regime faces several unresolved challenges. In this paper, we carry on important discussions about the above problem and present a new model that could partially overcome the discussed issues with a model-based planning feature and a reward normalization feature. We perform extensive evaluation on four weakly supervised paraphrase generation tasks where the results show that our method could significantly improve the state-of-the-art performance on the evaluation datasets.

Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning

AbstractParaphrasing is a fundamental technique for many text applications. Typically, this task is performed through models that perform lexical and translation operations, which tend to present a trade-off between meaning preservation and diversity. In this paper, we present a transformer-based approach called PTT5-Paraphraser, a PPT5 model fine-tuned on TaPaCo, a large corpus of paraphrases. PTT5-Paraphraser achieves good results according to a number of metrics, showing a good compromise between diversity and fidelity to the original meaning. Two human evaluations are made to explore the paraphrases produced by our model: the first analyzes their quality in terms of preserving the meaning and diversity of sentences, while the second compares automatically generated paraphrases with human-made ones. Finally, we perform a classification task, which shows that datasets augmented with paraphrases can substantially increase the performance of classifiers.KeywordsData augmentationPTT5ParaphrasesText diversity 

PTT5-Paraphraser: Diversity and Meaning Fidelity in Automatic Portuguese Paraphrasing

Manually labeled training data is expensive, noisy, and often scarce, such as when developing new features or localizing existing features for a new region. In cases where labeled data is limited but unlabeled data is abundant, semi-supervised learning methods such as consistency training can be used to improve model performance, by training models to output consistent predictions between original and augmented versions of unlabeled data.In this work, we explore different data augmentation methods for consistency training (CT) on Natural Language Understanding (NLU) domain classification (DC) in the limited labeled data regime. We explore three types of augmentation techniques (human paraphrasing, back-translation, and dropout) for unlabeled data and train DC models to jointly minimize both the supervised loss and the consistency loss on unlabeled data. Our results demonstrate that DC models trained with CT methods and dropout based augmentation on only 0.1% (2,998 instances) of labeled data with the remainder as unlabeled can achieve a top-1 relative accuracy reduction of 12.25% compared to fully supervised model trained with 100% of labeled data, outperforming fully supervised models trained on 10x that amount of labeled data. The dropout-based augmentation achieves similar performance compare to back-translation based augmentation with much less computational resources. This paves the way for applications of using large scale unlabeled data for semi-supervised learning in production NLU systems.

Efficient Semi-supervised Consistency Training for Natural Language Understanding

In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. Handling the full range of this distribution is challenging, in particular when relying on manual annotations. However, the same users also provide useful implicit feedback as they often paraphrase an utterance if the dialog system failed to understand it. We propose MARUPA, a method to leverage this type of feedback by creating annotated training examples from it. MARUPA creates new data in a fully automatic way, without manual intervention or effort from annotators, and specifically for currently failing utterances. By re-training the dialog system on this new data, accuracy and coverage for long-tail utterances can be improved. In experiments, we study the effectiveness of this approach in a commercial dialog system across various domains and three languages.

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Markus Boese

Papers

Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances