6 accepted papers
While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our inv
Motion compensation is a key technology in video coding for removing the temporal redundancy between video frames. Considering the incompatibility between traditional interpolation filters and diversified video content, the inter prediction method still has considerable room for improvement. This pa…
Machine reading comprehension of news articles remains to be a challenging task since the lengths of its context documents are long. Such reading comprehension task usually requires document-level language understanding while state-of-the-art, pretrained question answering models can only encode seq…
The prosperity of Pretrained Language Models(PLM) has greatly promoted the development of Machine Reading Comprehension (MRC). However, these models are vulnerable and not robust to adversarial examples. In this paper, we propose Stable and Contrastive Question Answering (SCQA) to improve invariance…
Multi-document reading comprehension task requires collecting evidences from different documents for answering questions. Previous research works either use the extractive modeling method to naively integrate the scores from different documents on the encoder side or use the generative modeling meth…
Answer selection task requires finding appropriate answers to questions from informative but crowdsourced candidates. A key factor impeding its solution by current answer selection approaches is the redundancy and lengthiness issues of crowdsourced answers. Recently, Deng et al. (2020) constructed a…