6 accepted papers
The few-shot relation extraction (FSRE) aims at enhancing the model’s generalization to new relations with very few labeled instances (support instances). Most existing studies use prototype networks (ProtoNets) for FSRE and assume that the support set, adapting the model to new relations, only cont…
Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLMs are required to continuously acquire new tasks. However, the more practical and challenging Domain-incremental CIT, focused on the co…
Speech Relation Extraction (SpeechRE) aims to extract relation triplets from speech data. However, existing studies usually use synthetic speech to train and evaluate SpeechRE models, hindering the further development of SpeechRE due to the disparity between synthetic and real speech. Meanwhile, the…
We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised an…
k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important research direction of NMT in recent years. Its main idea is to retrieve useful key-value pairs from an additional datastore to modify translations without updating the NMT model. However, the underlying retrieved noisy pairs will d…
In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper. We present BACO, a BAckground knowledge- and COntent-based framework for citing sente…