Recent research has highlighted the importance of mixed-initiative interactions in conversational search. To enable mixed-initiative interactions, information retrieval systems should be able to ask diverse questions, such as information-seeking, clarification, and open-ended ones. question generation (QG) of open-domain conversational systems aims at enhancing the interactiveness and persistence of human-machine interactions. The task is challenging because of the sparsity of question generation (QG)-specific data in conversations. Current work is limited to single-turn interaction scenarios. We propose a context-enhanced neural question generation (CNQG) model that leverages the conversational context to predict question content and pattern, then perform question decoding. A hierarchical encoder framework is employed to obtain the discourse-level context representation. Based on this, we propose Review and Transit mechanisms to respectively select contextual keywords and predict new topic words to further construct the question content. Conversational context and the predicted question content are used to produce the question pattern, which in turn guides the question decoding process implemented by a recurrent decoder with a joint attention mechanism. To fully utilize the limited QG-specific data to train our question generator, we perform multi-task learning with three auxiliary training objectives, i.e., question pattern prediction, Review , and Transit mechanisms. The required additional labeled data is obtained in a self-supervised way. We also design a weight decaying strategy to adjust the influences of various auxiliary learning tasks. To the best of our acknowledge, we are the first to extend the application of QG to the multi-turn open-domain conversational scenario. Extensive experimental results demonstrate the effectiveness of our proposal and its main components on generating relevant and informative questions, with robust performance for contexts with various lengths.

Event representation targets to model the event-reasoning process as a machine-readable format. Previous studies on event representation mostly concentrate on a sole modeling perspective and have not well investigated the scenario-level knowledge, which can cause information loss. To cope with this dilemma, we propose a unified fine-tuning architecture-based approach ( UniFA-S ) that integrates all levels of trainings, including the scenario-level knowledge. However, another challenge for existing models is the ever-increasing computation overheads, restricting the deployment ability on limited resources devices. Hence, in this article, we aim to compress the cumbersome model UniFA-S into a lighter and easy-to-deploy one without much performance damage. To this end, we propose a sequence-aware knowledge distillation model (SaKD) that employs a dynamic self-distillation on the decouple-compress-couple framework for compressing UniFA-S , which cannot only realize the model compression, but also retain the integrity of individual components. We also design two fitting strategies to address the less-supervised issue at the distillation stage. Comprehensive experiments on representation-and-inference ability-based tasks validate the effectiveness of SaKD. Compared to UniFA-S , SaKD realizes a more portable event representation model at the cost of only 1.0% performance drop in terms of accuracy or Spearman’s correlation, which is far less than other knowledge distillation models.

Session-based recommendation produces item predictions mainly based on anonymous sessions. Previous studies have leveraged collaborative information from neighbor sessions to boost the recommendation accuracy for a given ongoing session. Previous work often selects the most recent sessions as candidate neighbors, thereby failing to identify the most related neighbors to obtain an effective neighbor representation. In addition, few existing methods simultaneously consider the sequential signal and the most recent interest in an ongoing session. In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR). ICM-SR encodes an ongoing session by leveraging the prior sequential items and the last item to generate an accurate session representation, which is then used to produce initial item predictions as intent. After that, we design an intent-guided neighbor detector to locate the correct neighbor sessions. Finally, the representations of the current session and the neighbor sessions are adaptively combined by a gated fusion layer to produce the final item recommendations. Experiments conducted on two public benchmark datasets show that ICM-SR achieves a significant improvement in terms of Recall and MRR over the state-of-the-art baselines.

Session-based recommendation aims to predict a user's actions at the next timestamp based on anonymous sessions. Previous work mainly focuses on the transition relationship between items that the user interacted with during an ongoing session. They generally fail to pay enough attention to the importance of the items involved in these interactions in terms of their relevance to user's main intent. In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user's long-term and recent behavior in an ongoing session. We employ a modified self-attention mechanism to estimate item importance in a session, which is then used to predict user's long-term preference. Item recommendations are produced by combining the user's long-term preference and their current interest as conveyed by the last item they interacted with. Comprehensive experiments are conducted on two publicly available benchmark datasets. The proposed SR-IEM model outperforms start-of-the-art baselines in terms of Recall and MRR for the task of session-based recommendation. In addition, compared to state-of-the-art models, SR-IEM has a reduced computational complexity.

Question generation in open-domain dialogue systems is a challenging but less-explored task. It aims to enhance the interactivity and persistence of human-machine interactions. Previous work mainly focuses on question generation in the setting of single-turn dialogues, or investigates it as a data augmentation method for machine comprehension. We propose a Context-augmented Neural Question Generation (CNQG) model that leverages the conversational context to generate questions for promoting interactivity and persistence of multi-turn dialogues. More specifically, we formulate the task of question generation as a two-stage process. First, we employ an encoder-decoder framework to predict a question pattern, which denotes a set of representative interrogatives, and identify the potential topics from the conversational context by employing point-wise mutual information. Then, we generate the question by decoding the concatenation of the current dialogue utterance, the pattern, and the topics with an attention mechanism. To the best of our knowledge, ours is the first work on question generation in multi-turn open-domain dialogue systems. Our experimental results on two publicly available multi-turn conversation datasets show that CNQG outperforms the state-of-the-art baselines in terms of BLEU-1, BLEU-2, Distinct-1 and Distinct-2. In addition, we find that CNQG allows one to efficiently distill useful features from long contexts, and maintain robust effectiveness even for short contexts.