BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art
performance on sentence-pair regression tasks like semantic textual similarity (STS) …

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are
usually associated with intelligent beings. Argument and debate are fundamental …

Pre-trained Transformer language models (LM) have become go-to text representation
encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences …

The applicability of agglomerative clustering, for inferring both hierarchical and flat
clustering, is limited by its scalability. Existing scalable hierarchical clustering methods …

Existing methods to measure sentence similarity are faced with two challenges:(1) labeled
datasets are usually limited in size, making them insufficient to train supervised neural …

The growing complexity of legal cases has lead to an increasing interest in legal information
retrieval systems that can effectively satisfy user-specific information needs. However, such …

Hierarchical multi-label text classification (HMTC) deals with the challenging task where an
instance can be assigned to multiple hierarchically structured categories at the same time …

As the redesign process progresses in product lifecycle management, effectively managing
engineering changes becomes increasingly challenging, often leading to catastrophic and …

Pre-trained language models (LM) have become go-to text representation encoders. Prior
research used deep LMs to encode text sequences such as sentences and passages into …

Contrastive learning has been proven to be effective in learning better sentence
representations. However, to train a contrastive learning model, large numbers of labeled …