Given a question and a set of candidate answers, answer selection is the task of identifying
which of the candidates answers the question correctly. It is an important problem in natural …

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for
natural language tasks. Specifically, we first transfer a pre-trained model into a model for a …

Sentence matching is widely used in various natural language tasks such as natural
language inference, paraphrase identification, and question answering. For these tasks …

In this paper, we present a fast and strong neural approach for general purpose text
matching applications. We explore what is sufficient to build a fast and well-performed text …

In this paper, we analyze several neural network designs (and their variations) for sentence
pair modeling and compare their performance extensively across eight datasets, including …

Question answering systems have become prominent in all areas, while in the medical
domain it has been challenging because of the abundant domain knowledge. Retrieval …

In this paper, we propose a novel method for a sentence-level answer-selection task that is a
fundamental problem in natural language processing. First, we explore the effect of …

Automatic crime classification is a fundamental task in the legal field. Given the fact
descriptions, judges first determine the relevant violated laws, and then the articles. As laws …

Attention is typically used to select informative sub-phrases that are used for prediction. This
paper investigates the novel use of attention as a form of feature augmentation, ie, casted …

Large transformer-based language models have been shown to be very effective in many
classification tasks. However, their computational complexity prevents their use in …