Generating high-quality responses is a key challenge for any open domain dialogue systems. However, even though there exist a variety of quality dimensions especially designed for dialogue evaluation (e.g., coherence and diversity scores), current dialogue systems rarely utilize them to guide the response generation during training. To alleviate this issue, we propose LSTDial (Long- and Short-Term Dialogue), a novel two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. Specifically, we fine-tune pre-trained dialogue systems through using turn-level quality feedback in the first stage and further train ever-improving dialogue agents through using dialogue-level quality feedback in the second stage. By using our approach on dialogue systems, capable of enabling dialogue generation with both short-term capabilities (generating more fluent, relevant and varied responses at the turn-level) and long-term capabilities (generating more coherent, engaging and informative responses at the dialogue-level). We implement LSTDial on four strong baseline models and experiment with two open-domain dialogue datasets. Experimental results show that LSTDial achieves significant improvement, enabling to generate better dialogue responses in terms of both human and automatic evaluation.

Long document (e.g., scientific papers) summarization is obtaining more and more attention in recent years. Extractive approaches attempt to choose salient sentences via understanding the whole document, but long documents cover numerous subjects with varying details and will not ease content understanding. Instead, abstractive approaches elaborate to generate related tokens while suffering from truncating the source document due to their input sizes. To this end, we propose a Simple yet Effective HYbrid approach, which we call SEHY, that exploits the discourse information of a document to select salient sections instead sentences for summary generation. On the one hand, SEHY avoids the full-text understanding; on the other hand, it retains salient information given the length limit. In particular, we design two simple strategies for training the extractor: extracting sections incrementally and based on salience-analysis. Then, we use strong abstractive models to generate the final summary. We evaluate our approach on a large-scale scientific paper dataset: arXiv. Further, we discuss how the disciplinary class (e.g., computer science, math or physics) of a scientific paper affects the performance of SEHY as its writing style indicates, which is unexplored yet in existing works. Experimental results show the effectiveness of our approach and interesting findings on arXiv and its subsets generated in this paper.

Evaluation metrics shine the light on the best models and thus strongly influence the research directions, such as the recently developed dialogue metrics USR, FED, and GRADE. However, most current metrics evaluate the dialogue data as isolated and static because they only focus on a single quality or several qualities. To mitigate the problem, this paper proposes an interpretable, multi-faceted, and controllable framework IM^2 (Interpretable and Multi-category Integrated Metric) to combine a large number of metrics which are good at measuring different qualities. The IM^2 framework first divides current popular dialogue qualities into different categories and then applies or proposes dialogue metrics to measure the qualities within each category and finally generates an overall IM^2 score. An initial version of IM^2 was submitted to the AAAI 2022 Track5.1@DSTC10 challenge and took the 2^nd place on both of the development and test leaderboard. After the competition, we develop more metrics and improve the performance of our model. We compare IM^2 with other 13 current dialogue metrics and experimental results show that IM^2 correlates more strongly with human judgments than any of them on each evaluated dataset.

Rumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in Chinese companies with comments are rare; on the other hand, intensive interaction of attention on Transformer-based models like BERT may hinder performance improvement. To alleviate these problems, we build a new Chinese microblog dataset named Weibo20 by collecting posts and associated comments from Sina Weibo and propose a new ensemble named STANKER (Stacking neTwork bAsed-on atteNtion-masKed BERT). STANKER adopts two level-grained attention-masked BERT (LGAM-BERT) models as base encoders. Unlike the original BERT, our new LGAM-BERT model takes comments as important auxiliary features and masks co-attention between posts and comments on lower-layers. Experiments on Weibo20 and three existing social media datasets showed that STANKER outperformed all compared models, especially beating the old state-of-the-art on Weibo dataset.