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AbstractKnowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, ITransF, to perform knowledge base completion. Equipped …...

AbstractDespite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document. Recent works in context-aware NMT consider only a …...

AbstractThe task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed …...

AbstractWe present a comprehensive study of sparse attention patterns in Transformer models. We first question the need for pre-training with sparse attention and present experiments showing that an efficient fine-tuning …...

AbstractDocument-level Event Causality Identification (DECI) aims to extract causal relations between events in a document. It challenges conventional sentence-level task (SECI) with difficult long-text understanding. In this paper, we propose …...

AbstractWith the popularity of social media, detecting sentiment from multimodal posts (e.g. image-text pairs) has attracted substantial attention recently. Existing works mainly focus on fusing different features but ignore the …...