Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models(PLMs) on few-shot Natural Language Understanding (NLU) tasks by employing task-specific prompts. Yet, PLMsare unfamiliar with prompt-style expressionsduring pre-training, which limits the few-shotlearning performance on downstream tasks. It would be desirable if the models can stimulate prompting knowledge while adaptation to specific NLU tasks. We present the Adversarial Knowledge Stimulated Contrastive Prompting (AKSCP) framework, leading to better few-shot NLU tasks for language models by implicitly stimulate knowledge from pretrained language model. In AKSCP, a novel paradigm Cloze-driven prompt is proposed for joint prompt tuning across word cloze task and prompt-based learning, forcing PLMs to stimulate prompting knowledge. We further design an Adversarial Contrastive learning method to improve the generalization ability of PLM for different downstream tasks. Experiments over a variety of NLU tasks show that AKSCP consistently outperforms state-of-the-arts for prompt-based fine-tuning.

Stance Detection Task (SDT) aims at identifying the stance of the sentence towards a specific target and is usually modeled as a classification problem. Backgound knowledge is often necessary for stance detection with respect to a specific target, especially when there is no target explicitly mentioned in text. This paper focuses on the knowledge stimulation for low-resource stance detection tasks. We firstly explore to formalize stance detection as a prompt based contrastive learning task. At the same time, to make prompt learning suit to stance detection, we design a template mechanism to incorporate corresponding target into instance representation. Furthermore, we propose a masked language prompt joint contrastive learning approach to stimulate the knowledge inherit from the pre-trained model. The experimental results on three benchmarks show that knowledge stimulation is effective in stance detection accompanied with our proposed mechanism.

Responsing with image has been recognized as an important capability for an intelligent conversational agent. Yet existing works only focus on exploring the multimodal dialogue models which depend on retrieval-based methods, but neglecting generation methods. To fill in the gaps, we first present a new task: multimodal dialogue response generation (MDRG) - given the dialogue history, one model needs to generate a text sequence or an image as response. Learning such a MDRG model often requires multimodal dialogues containing both texts and images which are difficult to obtain. Motivated by the challenge in practice, we consider MDRG under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model. By this means, the major part of the model can be learned from a large number of text-only dialogues and text-image pairs respectively, then the whole parameters can be well fitted using the limited training examples. Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation, and can generate informative text and high-resolution image responses.

When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a ”free” warm start to the topic classifier.