Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Zero-shot stance detection (ZSSD) is challenging as it requires detecting the stance of
previously unseen targets during the inference stage. Being able to detect the target-related …

Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the
inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework …

Current NLP models heavily rely on effective representation learning algorithms. Contrastive
learning is one such technique to learn an embedding space such that similar data sample …

Despite their recent popularity and well-known advantages, dense retrievers still lag behind
sparse methods such as BM25 in their ability to reliably match salient phrases and rare …

Abstract Aspect-Level Sentiment Classification (ALSC) aims to assign specific sentiments to
a sentence toward different aspects, which is one of the crucial challenges in the field of …

Contextual ranking models have delivered impressive performance improvements over
classical models in the document ranking task. However, these highly over-parameterized …

Retrieving evidences from tabular and textual resources is essential for open-domain
question answering (OpenQA), which provides more comprehensive information. However …

In this paper, we propose to align sentence representations from different languages into a
unified embedding space, where semantic similarities (both cross-lingual and monolingual) …

Abstract Although Question Answering (QA) have advanced to the human-level language
skills in NLP tasks, there is still a problem: the QA model gets confused when there are …