Generating vivid and informative responses (e.g., comments for social posts and utterances for dialogues) is challenging without giving relevant knowledge. Prior works focus on constructing the ”latent” knowledge first and then learning how to ”ground” it based on pseudo (context, knowledge, response) triplets. However, the retrieval between real responses and their latent knowledge is difficult in nature. In this paper, instead of focusing on how to ground knowledge given the responses, we take a different perspective to optimize the final responses for given guided knowledge directly. This allows us to re-formulate the entire problem in a simplified yet more scalable way. Specifically, we pretrain a response language model (LM) to measure the relevance and consistency between any context and response, then use search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the ‘‘best” latent knowledge that corresponds to a given response. The final response generation model is trained through reinforcement learning by taking both the response LM prior and knowledge-injection rate as rewards. For better evaluations, we construct a new Chinese benchmark, ”IceKC”, using fresh multimodal online social posts. Both automatic evaluations and human evaluations show our zero-resource approach performs significantly better than prior works.

Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, there is a growing trend of applying prompts on pre-trained language models (PLMs), which has exhibited effectiveness in the few-shot flat text classification tasks. However, limited work has studied the paradigm of prompt-based learning in the HTC problem when the training data is extremely scarce. In this work, we define a path-based few-shot setting and establish a strict path-based evaluation metric to further explore few-shot HTC tasks. To address the issue, we propose the hierarchical verbalizer (“HierVerb”), a multi-verbalizer framework treating HTC as a single- or multi-label classification problem at multiple layers and learning vectors as verbalizers constrained by hierarchical structure and hierarchical contrastive learning. In this manner, HierVerb fuses label hierarchy knowledge into verbalizers and remarkably outperforms those who inject hierarchy through graph encoders, maximizing the benefits of PLMs. Extensive experiments on three popular HTC datasets under the few-shot settings demonstrate that prompt with HierVerb significantly boosts the HTC performance, meanwhile indicating an elegant way to bridge the gap between the large pre-trained model and downstream hierarchical classification tasks.

Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.